{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# 多类 SVM 练习题\n",
    "\n",
    "*完成并提交这份工作表（包括输出和外部支持代码）。更多信息，详见[课程网站](http://vision.stanford.edu/teaching/cs231n/assignments.html)。*\n",
    "\n",
    "本练习内容如下:\n",
    "    \n",
    "- 为 SVM 实现一个全向量化的**损失函数**\n",
    "- 为其**解析梯度**实现一个全向量化的表达式\n",
    "- 用数值梯度来**检查你的实现**\n",
    "- 使用一个验证集来**调整学习率和正则化**的强度\n",
    "- 使用随机梯度下降（SGD）来**优化**损失函数\n",
    "- 对最终学习到的权重进行**可视化**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# notebook 需要运行一些启动代码\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 这是 notebook 的 magic 命令，可以让 matplotlib 的图表\n",
    "# 显示在 notebook 中，而不是在一个新窗口里边\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# 还是一些 magic 命令，让 notebook 重载外部的 python 模块；\n",
    "# 见：http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## 加载和预处理 CIFAR-10 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# 加载原始 CIFAR-10 数据\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# 清理变量，以防多次加载数据（这可能会导致内存问题）\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# 完整性测试，打印训练集和测试集的大小\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U7Ziq16sYYx+7vd0mTW1B5+dn8XU9cF0p1trAd+n3bR/1uxGOtk+WuszMNPVdMWlqn+m5lWK9TNMIowtOmmXFu7JMCIe2rX7v//vgVes4ZXimmGKKKaaYYopXPW6YS+taDM+12JVJYMZOudd63vgzx++/2vdXYlz6vRHCNMZXsdl0+0T620qjWUimpbkmZT2Vra+eJliwUny1Oc/szuW8NMUz7zQp0qQJ5Yots1erkya2jsYxoBI3RijqcI3ulGtfGn8bxRGjyAGn3+Zfm6sfMESEJLYSejQY4M9MlurNcYQgCIpnxJGeiExWjJHLx4QUFRFHED1ZZ1kyOmW7HqLMTCYUn42xDJ59intVek0c53LmrCjo6L2ITJZcRVEuDTi3uQnAYBCxY8nGAqzPuCzstnXfWa3RWLQxOdcvrLN+yqbp2XzJwy3bU9POB+6GVcsOba1dZH6v7f/GYoNkqG0Uz7C1vq33dEgzW/e4n7Bx0c6DKEyp1CwjVK5XuHDalm04WGdx2Z4Mm40UM5g8hZFxDEHFnvQ8zy14UqeUEdSVbcuEONJ5Wa2SOvZzksYYHdd+GlDK7Mm/4pQR337vlg0Vx7ZVbzBgfocN6jq/2OT5F54HII0NbqpjJoQ0tmXoJQm+npZ9J6BatnWslHwC/yY6Mgw58vxnAHjmyc+QhfquOIWxMZrou/rDqIgSeKnTA61vo1KmWrZttTA7w65FyzjNzc0w6Nv+7SSbBPrjpYUFhsqYbHWGZI5tz0ajhqvs6MW1NRzPPr9arTGvTEqaCoF/WTy4ayKWCEdj4QWSMdR18wsvvEQnsif6ek0IB/bznpUZylrIrdYmgWPXqYpXJVO2jwx8LZfnOoiuWVnYIUxtXaP+FlG/Y8sQxcRRomWPCZX5cSQjc5UhIyVWNqDVjyiFNzFOx1hkoCiPK4ZYx45fgn37bCaIoy+tMlRGKxODGWOdL3+u/X+Kp+sxgBnRztno4/hvUxyynFEmQ/T5wpXPv5l92tDTcRTHLuWKnTfVagUlQ3H9jFLZvjeKIkTH5sL8HK6XM+KGNLU/SFNwXad4fqmkLKxXJo5s2ZIY+jpmHFfwXDvGB4M+SWLbsFIJCMqWcTSJIWcuRYRms3ndWl13R8mybKJG+mIVIeNqhpd3/sufOmmH3UzHBksHcFK7OFabAe1OG4Bu6hOUlwA48vwqwy27oJcqO0hqswC0y2Uypf0SDGK0M8f2/Ek3tasJgbcKIkK9bgfCsFUCpb/FLY32ZQHJJ9l1inBZMqmrtvNI3JPx/f06Zbvssy4kTlDB9f1rF2QMnucxHA5ffiEbBZR1HGdsrF2ucso/u16AU3DnDmmhupKR2tYITlHt0W9NNlKfif3Ry56PIyRFmQzmJiTjoD5Dd2DVqsNhRLVmp27YhY1zdqF3Ap9KzY7fpQWPwZptv3PnYrySbR//xAU21m0O1FIzoL5gN7XOIGD7iN0AlqtVBl27IXVaMUaDnyYDU6jwsjRFVBXheIZEF7X1C31KnhXGFhaqhN3JvbwlS3C1DUuOi+eperkEXlUFzlhwS7psGQdPN0JjMjxs2RpulYVZq0opl8sMsBve9nCLTNVeni8MdUHf2sgwKiPHUUZraO8Pey5ZYhflJEkxsZ03FUoEqmbK0uhm5FY+9IE/5MXnvwBAFKU0Grb9Pc+jvd3Sdsjwte6I4OhGIgKODj5HMqraDrVSWccfbLS2qerhxogU8yKYdyjV7PebrRZJausyHMLyolUd95qznF/fsO3W93FEN6SghBtMtqYmUUJQ0h3RGKLQvmdmboZNTZPZ6hjqFRWikwFl7c4kGTII7Vg2iSHTxzji4KuA5nleofqJe1uFejkM+8SquhqGMb2+HpziiDS1gp6QkGhv+V4JV+wzjWuKcTQJLj+QG7JiIgs4KqhGEW7ga71ijNFxSqH9xRnbKOwz7fdp5oztqVmhqpXMkKWjfSJfVzIgzzTqmvy5XHboupJEuBFKpTJhaMdOfzAs2tz3AxA9MGcxWZqPTY9Bz5YidFIaqhb2fRffzw+LabERRCGFuirwfapV+5xeb1AU23Ey0jQqypM/J80i4sS+ywC+Hnaz1GCKlrg67jQyYooppphiiimmmOKW47pi7ZXUXQ4rWI5kpS9FJJ9rMTzXMngu7jfmpsrTuec11Pfb/Gdh4OEVhoM1Bkq/9TbWqOlpyhNDX6nzLUkpb9jT8mt27qRUSNaCkZF66EaszfXYrVvC+BiDUSPFYZRSU5WP49cKVsfIzasmr/oqRlSqmeDse1ldXZcktIyAk8UjQ+Eb4K1v/RqGSpH3+302VfXTbncKw8ckScbGsiHLedlMyA2eXfGQ3FjUzXAuU6XaE45gaNStkfAwjIkSratIcaJyxeDo2KnXanS7lqKNU3M543UTzX3x1Daip/ulPcvMztkTVJplbG3Y59dqLmefXwMgCCio54XFGWplq37au3SY+cAyl6dWz7O+bu8vNzIqSkMvrazgOZZtOH4qwZFI32UKQ+5q02V5r20Hr+Lie7Z9zp9p0xkq27AZs7Bz8jNV5gpuoIaJTkos9ple2aPStNS2kwV0urZicTcENU51XJ9v/EabH3LvzmUuXbD18oM6p9fOA2B6TqECwSS0dZxsXjJUKqqiqvjFCRbXELb0ZCs+Qb72pBBr57mVgEq9SAh9Q5w6egT0ZDs/t0im5/3eYEhfGSQxGa4yMJ4b4Dq2DCXHpxfa8bwZRiShbQcPB8/VcZhGZIntl/mZGSqBrZcjDkFJ1UV+iXRgn9NqdwqWJM4gUfa33U9pqFF0nCTEUXuyChpDVQ3hJQkLtUgSxQSeXYMC16FSsuPCNTG5MqleqyL6VxwbPGW2gsBDdD4N44gw0v7P0jENsUOixqudXkS7Z9mJYRJR0hcECPjKIJZdJNN3hSGt9uRM5GWaD2OIJWc5hFgZXMcvUVH2DjKMtvGIBx5X9l/O8BgjI40WWaECk2zE0hhjCmcII1mx1hpGxI4YuLndcATPzyhrP/p+nUjbHElxLaFCxfeJItvOrusxq6wqQBRZlrTXjUbjQSBNbTskcVaorE2akqmDhTEpnqqI0ywljNRwPRjt/Y47qpcxWdHvaSL47nhu0qvU63oXs2yssXEo9nBJMZmttWsSjE7I7KbIXYub2dDHabksu1xXWTxHhOwaNiJXw8dOnGX/LpskOewmdFq2A3uDDQKlhjEpIrYDF+abRLHSqFFI53MvAnD/a+4rbFQQU9iFTIIr2+DWq7UyxFNqvhcSbVkhrVKbHxs4N9a+mTQhU9WF65cgr6PVEdmyk+Do5E7xuWxXLzRm137TsG0p9SzLrNvUBNize1dBSdv/q145jOmosLG9tUWrZTfx1naLvnrrxHFcCEJJEtMf2H7OHPB0c/cNjDT8htmGFR4ae+Y5cfqs1tXBU/uDNBqwd4/1cqrVajz7rFVhOJ47WqAN17AAujrOHFunOmOfHw5DUv1tpxsz6Nt+8JyYNLdfwqWliz5Rwrd+gxUG9h+8h7/+xF/YmkjG7v223e59sMygY+ve7XbZ6lhhoNqs0l6zi872dg+vYlWjBx+4j6rV7BK3OxjdKB1X8Cr2c9Csstm9/gI0jkqpCp6OnZIh0+kX1H2GPV34oqRQRbjlMguzthDveevb+KrXvgGAuYVFUrXVObfZ4eQH/hiwKrlc7RwPM1Avs4pTIfBsOWd3NMmwm58xGdU53US3h8jQvjfpp+S7aGQM8eQmg+xY2EGnZ8eeFzg4ugSXPNcaOQC9cEBPF/p4MCjWEiMemX4ehEO6XStMeI6QqP6nXinj6rypZEKzZoUf3yuRqNpr19IOuj1bx5Pnz3H2nB3DlUqNuZrdpAfDhFif6QbVyTdOM/7RIfPsZhdJilENtVcpkRj1qEqyYt3MYuipOjGNI0q+zqeshOPaMZUZU6jj4iQmzu0RkcL+KMkSOipoZQ5USyr0mZRI23U4yNRzzaori9PBBMiy7LL9ZqievQ6OPfgAKUJaqMqdkc7JjKmisrQQTrIsKzxUM3HJFV+GpFBpXda2xhReS0mWIHq/63hkegjLzOV2PDdj21qpGeoNX6vo0G3b3w6GIaWKbWc/CAjUhieOBwxDu76a1CvWA8/xifUA5Dg+5bI9HMzN1JmfXwRgz+69BGq+4PtSHOx6gwHD0K5n/V6fLN/705T1Dave7/Y2iGP1vIwNEl9/z5iqtKaYYooppphiilc9bqjSKj5jCoYnM2OeNiYdo+tuXn66kReYiBR+9gAlpVnn5uZoqJdImiZcumQp7E6nU3jUTILArZC69gQwDCNCtQQfDlMcZUVm5hr0h5bSzfy6pb0B2ts01GDKz4RMaeVUTMFyiJErmI2r17egOTMzFudFcLUur4T1Gac/4xSMqm3EjBlLI2NuVJl1TbIXCjUPKaCeFsYPClXQOE9bNqn1/gIGximMkEXMiAE0MnoVpvichkMyjSFSWdw7cT/uXF5ioDEdBoN+Qb+KODRq9kQx22yM2iCKiPXk0O12CkpUgK1t28/rWy26HWtAGfb6pOr1EcYD2l17Cn3Ng49w7uIlAJI0o1axp+n13pAksZXqdUNCNdz0XbfoWzJTnAAnwaAX0+3Zdj1zaq04BTmOi6tsRmc7oRTYOpbLZTa37Xv77Tbdjm2fS5cu8tSTnwYgqA6pz9gT/fq5Gn5sKenPHfkCWz1bL8eL6YW52s6nsWC9wDqtMhtn7IluacUnitRYsweVGb3f9yg36xPXMTNZwSBWqjVcjaWDyXD69pnDfoLo2PQDnwX1Ttqze5n1S9YqdnurzZve/m4Adu4vc+riSQC6n71EFKpxZJghyoSU3GrBogwGEY0Z+940G+L5tg2XdjQZXrCM2cCkVAv2z5AVrNqNIQIl3z6/XAoo+XmMEo+Kfl7b3CqMdNtmwFC9lbIsIjW50bULyii2+gM6/b4+X6irCmFlcYGFOeuJVimV8dTrVEiZVaPSlWwBs5afnKGq7y1XPM60bf8mgwHL+pwbodPt4Ld0DPoOrrZrv7VJu23bcnb/fja37HhZG4aUAzsGq/7IyDpJfYrtyXFB1NPHpOTsh+u5BOqp1h8M6CmbK45HtazmCGHMYKhqTN+hVlVvVUcYKlMUhiE1f3Im8sqYXpnOvyxLEVWHZrEhjnNPsWTMaFlGqv6x9deMxQxzSApjXccVyPJ4YEK+CXueR6KGu47EBJKrddLiXZd53t4k2q1use9WK3VcVRXF4ZC+smfNpkujaRnW+cVDlMuzWn6fOWVed6/span3zM3NMz9nWZ1mY45KWePZOUHxrv6gS1/NE3r9kO2WXY8vXrhYmAZEcUSWWlZyff1xIvXsDdwS7g3qe1Nu6YU1ugQYUW8NQkxBld0aVcy4+3kcx4XL8e7duzl8+DAAvu8jSiXWa3XOnDkDwHPPP0+nuz3xu77jvW9C1HbBdf2RlzYufnmkmw2j3LOJwrYji0JmGnZxubixzoV1S7OdvXCBTlc3y27fupwCb3/722jqQAjDIVvq+XX06FHWNLBhq9Vic9MKb48++gjf9m3fBkA5KKGaQ8QxxYY3rmy5JsxIaFnrDKkqe1tzwCjVKgK5z2MmgufnlvgumQqB6XDAQMvspwbRRcJkBpO7kA47OA3rpZNUAzzVvZtMcHLKlqgYM67rY1QHPuj3cQP7zGpzliSdjIKtlYJis0gbNfLgdWMxt3BdtxCgHJHCxibLRouX73sYbc9hlBVCVL/dYUu9VzZbW7S6dhKePH26cOnNkpRO2467RrNJqALVertTHA6SJC0CZskEKsRxtLeGdLtK68eQ/9oN3OLA0Q+FoaNCjtsiTtTLKU154omPA/Da170JlSkI1wznVZDYmOvy2rv22e+ji/RUQOp0h9Tn7cJUr9cYtu3YvHTqJRzdMO5//cGRAObF1FSISsKEk8+tTlzHclkQVS0Fnl9oGZI4w4ltfUtuiUTHju/7rG3Y8jz2sQ/TUDslEwqoQPLQIw/y+vvuAuDYsed47kUbgNP3SgQ17btUikB4yVbE4qz1WjKuR2eQB31L8VQnszjXwNOxX27U8EqT96TrCIGb269A4OVrjEuzbtvNyYRQ31vzerRVmInJ2OhYIWQQxoUarhPHxZ/hxKoAACAASURBVCaapBnr2net/pCZDXt/rVxmbs4+f35mBpOoutNAvWrbzXU9AskFngpGXX/PnN8g1oPIjTA7UycK7VyJ+glJrseKu5T12ZvnTheHkvNb3cLrsXyghqfzaRDFpEa9qzwP38/HsimCRCZhTJJogMEkxlNhoFoSZubtGtTu9RmomswTg6/roJOlJPmaZTL8yb3Sr7CxpNisSx64Orkc12HXDiuMH6+WGai9VQZjp1tzhaowtwFM8dUrLk0MmGB0NReoxtRTLjFG28FxakQqIGUjn1P7rpuw0fT9KsOBvb+1vUUp0FAs1SaDgR07GxstfN+ag9x395vZtWLd8CuVCtWKHVOloDTypHRc1jZtuV988flifXacCq223S9b3VYhKFovQfXMSlPiWNvHLSNY1brrlPF0//Z9Q618fc/eqUpriimmmGKKKaZ41WPiODzjKo/nXjjOulKSX/XGeyhpMKdsghP5tQx0xw2SRaTwmti3bx9LS9arxHVdTp48CcCJEyfZv2c/AHfddTe1upUoH3zgAZ58+rM3LEeOe+7aTUdVF+MGrKlJMSpNlysV0tRKjmEUFgGT2ts9/uKxD9jvk6xQgZ04faqIsTJXb3DmxGkAzl3aYH7Hoj5nRA0OBv2CEfBcl0RPQh9+/JNkSn+/9z1fR7MwvpvE/2kMgrXeAyr0cTrWawVzcMyoTQojZC8e4IaWPuxEGd1tNSQOB2QqI5ccl0xPTm4GqW/bv5fGZD17v+Nt0Vywpxy3Wi88fJJeb6TmqwaItnnWOoevDI9BJj6RuGOpHHzfp660tYwRnMIVbOXYOHTGUqPkBu9uEDCrqUW8XUtw10FbdoSXTls28VOf/DhveN3r7Htd4S8f+ysA3vGOtxWnlKeffhq3Y9+bpHFheCfG4KlB6SQo1V01ZoRSxS/irvT7IZ7GxilXAlrKwA3SlDgd1evCeZtOIo5iFpaseiLpddm7eDcA3bTLiy8e1d/2cDREfq87pFxXGr0pDCPLYt37yAxzyzmVH7P7kE0JcPiNC1w4aVmFSyf7zNV3TFzH5t5KwdqmCaBqsiCoFAavWT+hosas4rpolHvOb20Sz9iT9nJzB64e2T2/gqNs5f5Dj9Dv288bF0/S7m9o25aKNA1p5tLt6lx0BEeNIONBxJsffTMAi4tLPPXMUwDM7ZkjciZjP8AGbivPWPYhjmJ6eloO434RbydOsyK1xEyzSaSqi4CEtBgzA1Kt/DBLC1VXGGdWBQTEvYiNrmWOS55Dc9u229LsDFX13hLXQ9R7yvc8EmVPzl08x8GDlk2vV5pstroT1S8ZhMwv29P3oNsl1vk0Uwvo9YdarhDJ0wTECSfO2jI2K3Bo2Zax6aeApjOIHJI036pSvDySjckIVZ1ocPB13hsPUuy7mvWAxVmrvgvDkJKyayXXw1XD7Sg1lIKbU2kVqijAU3USgy4NVYF2t7pkPbuvDIdDYrVstyk9ctXV5SiUZNmA4dC2d8lvFuYFmRjG/XHyvSpwYg4ftEzLiy+tkU+Wcc/RvNyTIggC+n07FmqVRhGHp9tt09DgftVGgOtoWptug1NnbHvGySZpMgowOPJtSQvtxfnTL3DvPXbtWd51oAjMaByDo+PXMQYnTyFhTBEU2MEQq1F0vV4qWMQkGpDeILjZDW14xjs2D3p1ab3D4586pYXdzWsfsAtoa2u9KOzVnpVj3MI9VVrcGKir0LJr1y52714pvn/xResJtbq6WtBj5XKZRPWcZ1dPMVC939bWFsPwKkHorgNfLcTTNC0+x3GMq2oSRyiC0LmOy+mz5wD4vT/4A9bX1Zul3mCrpQHgPBdP3WuXdyzzjne9E4Df/K3fpJe70fp+ISDWG3WqVTsp+/0+pvDECDn1u78PwOLiDr7+rW+xBc5SbkohYkxBu1YqDo7q8rNsTJ8MJOqh1Lt0llTdwyWo4qmu1Z9dxCtbWtwNAlDduJPFZOq9g4FYXRWTfp/Btt0g0/U1yqrqIosJ8s+Gwq4lDfv41YYW2bwsUui14Pt+Ub/xMes4UtgQWHk9t4eiCEA3HjZAREYCiTgjr7ssI1C7rUFvwJGjRwCI44Seqr3IUkoqkJ5ZXS3mSrUWcLcKS3sPHaal3mFbGxvE4eQb5UNfs8Dzn7WCxPr5fkErp0mCo8HOTJQVHjqZcUlUiPZ9n41tOzYvXLjIm974VQCcOPJ59u+y8+zoyTM8f8Kqe2YWAjY37FgY9EMksGpYf6bG7IytY6laLlSdvSQjRdUipV089YkXtWw+994/+UYys3OG9pYdU+FWn0BUsBFB9JCRJYayLmqO42L8PBBiQqJ5o8rVKpEKhJ999ihe1Y7N737fdzP7Pd8LwOnjL/DRxz8EwMee/BjrKtQ7joOPfe/9d99HQwWDQ7v3sXPBRlX/0Ic+XORqunj+HLE/uYdPNfCLNWC73SEM8/xrbuFa3u13mZm1ZfY9lzSr6Pd9RO12DA5bXTt+Sn5AmqtNTVQISP1hjKjOuuc4hSt9mhlKnm3nuWaziMKetkMrbAGr62sMdE4dOnBPEczwRjAyOhhXq1WG+SblOKTqIeX5Zc7outkOU0L1tHr6+CXI7Pw/tFTG18CWw/4QHB135TIlXb8CT0hUzbG+3WWrZesknqFWVy9iKHJpVUs+cxoo1s0cfBV+YtcjiUd2ojes49h6KiL4amvQ9BNUXiM6t06mZg2dThvfs3MoTke67Jd7vunBQmJ27rZjbf1SD6OH1VRSslylNZZAa2V5nofutaFVjp24MPJkvixI6s2ptDxPisCfIlLEXa03yvgq1JVqPs2GJSNmZnaTaM4vyVzSRPfULCa3ExEHZuZtGXw5RLMxr28LCi/DOBvZdNr8cPkibMj5FAch1UihGR38kuZKc1MSPYRfC1OV1hRTTDHFFFNM8arHdRmedDw0P1IYGR3Yf4CnnrGGm6fPdjm833p3VKtNumpU58goeNJ4AENjDJlKx2EUUVNPqwMHDnD33Xfrc6qFBP3UU0/x4hF7YlzasaNggQDOnbcGke12i3PnLOvi+wF79u6buAGSJKGsGXQrlQqhnrqr1XLBJoVxUqhaTp05y/t/x7Iuzx85zuycrbsJE7Y79oTRaDZI9ZR19tw5FvWeTr/L/Q89aN+Lw959ViX3ute/rnjvX/z5X+AGlu25/6EHeeoJ61Hzkcc/xX2HLMW8b1eev2syiOOQ6ilHPI9KIw+UlhXGiGG3Q1+NbsX1qS9bVUSpXMFRtRoyniPKgHolmTgiGdh+9/wS5ap+X60R53Fwzp0h3lR6veQTNGZfXlC/PHHsnXF4rnuZarSgRGWUj8nmzBp5nnkja77iOY4zijUlRnDH6r16wXotfebJz3L+vFVRZimsaoA7z/VZ3m2NY7uDiEFoYx0FAvccst9/3Td9M5memrqdTuF1MAn63YQgz+1W8ot+aC40qdft+D13vs3CgmVbh/0h4TlbBt93GMaWEfrsE5/Cd78agN0L+3BUbbCxuUZF46SZzNYn/62jKo/6Uo1du3MD9pRhx47x6lyVRJ9/6vknaHh2bQgqFfqbX5i4jt3OECdX25XKVDwdp57PUE/ggVfC1WXJcxz8XDViHFL1JtvYanH8lGWgDx5+gEfvv89+XpinrHXZ94a38rpHbNye1376LXz0ox+0z/cd7rv/NQDcdegwmeZbeuELz/F7v/tHAKyunqOqzgphGBZzdxIk8bBgFpYW5omj/NjqFh6C1XKVSI2WN5UlABgkplBdOa7BCzQGSpQSqXdTmmVFDJS5Zr2IDXVps8VmWz1Qw5iGMjaJgRld2MuOR7ttx2S712dTVf2DQUJQmiy4YnvQw7SU1TEpic6/3jBks23Xx9b2RYbah43mHGVVWzRLDTo9HctrMFfLmVcb+w0AN8MZWyMSVYFmUqKrQUYzydAMDFRNUphclANBE5jT6w3oatqTNHUYJjdhtcx4QF7BjTVHWDnl7Y/Y9f1kPaCqMaX27d3NiZN2Lhrn8thkBb8uUpgLLM7P8vADBwD40MVnKWLyZKYIVCnGMD+na2h4kapv+y1L4sIUg7GcgdaTdnKtQBzHdHUsZJmDp23uuFAu6xrW9ymX1BPNHdqs8EA/zEh0X8nSmNzZ1nPF6huBan2OUNVeWZgSqldraswolQZSOIUYkxVqfBGDlzssOfMkeR6uJILkFai0Pv/s54ocQiVxi4BfnX5KrWo7+dSFTZ580W7orzk4Q2/Lft/dWke8UeCl3N3Q9Vx26KJ8+OAh9h+wVNzCjiVSHURbrS79nqWmgnKVgwcOABBFQ06fOgHA2to6W1vqsTAYUlP3453LzcLDZBKUSqXLLN5z4QdGaefL5QrHTlk3uN98/+8w0MV3x6697NtvLdO7nQ5tnUB33X0vO5YtXZcMh5TKOTUPHdXrLu3aw6aqe46/9BJtXVzqzSbHTpzR56+wd7+t+4kXnuNDH/0oAN/1bd9KfUKKGaB9+hn8kqolcEjXbV163gI9DV5mulvUZ22/lBdXik3uMh1wdjntm6q+/8wLT9BetWoep1Rj1/1fA8Dizt34KtDO7t1jPQ6AZDggbGluMuYKzyxHQlINIoVZnpiCFcfBvUoiWsEtojU77uUTwXFH9mIy9pz8L9fxigXx6PGX+MwT1mbj3IXzhQqg1+vTymx5fS9gYX5J30vhhRIEHhXNqeO5glPKXZLn2b1710T1A3jp2T6+lwf3y6iramnl4Ay1hrpRGyEJ80SfPSoVFfyc0UbZG2xz7Kg9QOx6w6Ogaqn19bMcvt/OoY1LLolu6INBQqAb647FjEBsm3gzDnM7rIR0/mxCoILcyoEdhRrx6OdPM+fPTFzHKMooq+dRuewV7ucuDn7uzZ8lxMrTJ8SFvYpfCshX1jCLKNdsm991cDdl3Sy3Ny5R1cBnSZIWQeLecP+jPHTobm3DuHDx/uyTT/Dxv/4IAKdeeqlwf63VaoUgIa4zyns1AcrVSmFzFQ4jepoosR+ntFXIGYRDuhqccBDGGLXt6SdhEXX5gdfcg6eRk48cPU58UdWXbomq2rIc2LOz8Px6/NNPc2lT7fLSsPA67faHbKvg3SxXimB2ruvSVwHixNnTzMzk6ofrIzWGJM0PHGmxQXueS5Tk6jthRoX3+Rm/OFg2mk1cbZvt/pDNbdsP5ZJLpaaeOFFUBETcbHdpaYiILBOMjgXXcegP1HaonLHUtGN5o5WwutHWMkBNQzhkcUi5epUD2LVgRutgZgJSdZl3k4hAzSlcuuzbZ/fFmQMH+e3f/lMAWv0Qk4cqNj4lFU4iA7V5e5CVbJUdzS19gUMWaB3DMrnctzJTJ2vbe5aW1ti9O090XMHkOe4iD1EPr8ykRXDCSdBpDUhU1Vgt16joaSiKhqTaj0kYkGpf9+JtLqznYyorcn55nlPkbiwHAZ7O3ZoDefqyME1IVBiz9pwjb+FMVbIOGa6Y4nNZD0Mru19HqZyv8YZK5fqC+VSlNcUUU0wxxRRTvOpxXYbn9JGjRVqZ1BNEb0/TDF+9OHAHHDtlvX4urZ3mkbutF9KFjU3CobIW9Ro19S54wxtez/333gNAUK5Q0VDmrXaHbk/T0UdR4TnV7/foagbzs2dPc/68fZfjOGxsWENDz/VHarJhRKk0Ofvhed5VmYQsywrvhWGc8YHHrIFjdXaRud3WoPD8xTXqany7sbHJTNPWZd+ePVQ0M/GxF1+geZdVRbmOw6kTL9m6BxViZYrOvPQS6xrDZ2FhoYiNc/S559ixpExRkvD4J6166+H77uP1jzw0cR1feuFzlDU/Ce0L9NesVF4bulRW7gWgvGs/NW231BnljrqSBs3bynVdzp49acv55EeZVcPN6vLBUbDEzBRG0Y4YvJIyHeUq2bpVRw7XTlNRg+1y0iVO7WnM3ISRnee6hbGxLWP+wTKKcHm2dCiIgcs8tESkUIetbW7z4Q/Z0/251QtEeiKulGuFt9lgsFkE//I8n6EazC0tLZHo/QmGnAAo+Q6Ol1O042q1G2N5ZZ4gsH1SW0wRNVRuLvtEqk6qNlwurGnaAvFY3DGn5YwLI+2dy01cZbeOnDjFwoJVXcaRQ+eCLej6+S4tTeVQadbYuduefpNhjyFaBq/JME+vNOzRUuYyGS7R2rYV6/RSjDO52s4Vj3CozBhOEUhJMhdfM6HHLvgaa8M4hkT7z0GK+52Sg2Cfs7F6krqTqwIDwtygt9/jYq4Gdxx8HZsvHHuRzzxlvTxXz51jqJ48nusWp0rfL5HlbKdLEdByErS6A/p9ezIfDiM6ykavtbu0VX1mPIe+Og2EcVJ4DnqucGiPZQFWVlZoaO6i2flFLmrg1a2tNhUNxvfQA/cU+ateOHKS9W0NwOe4BZs+iCMG0Sgrdl2Z8sxkuGoQ3h+GbHVbE9XPFY9QvbGqFQdHcyqsLDRY1dQocXnksFEWQ7Vqy7u5ucFmK2fnHXIr1TTJSHTuxpkZC0RrmG/Y3y42y4Q6D0RcYr1/tRtzftOO5dQNuLRp237HfI0DS3a9XvSFLJrc0UUwRa5E3/dZnLdzaH/VY+cO2z/b8VrhKTZXL1NVlrTTT4lzBxhxkMj2yY7lHeQtXPYjdi/av7IkYTtSg/p0kbl87PfWcYf2nrt2geda1XEmD2Fcuw75GfiFKsof5XecAPOzs8Xa6XnemOajhuvaMRKbWmF+ceb0ebZa9vlRLAU75HsOfY1bZtKUnctWi1BbCgrWPckyjLK5JksLZ5VsFLMX1xup54wx1Gfs2H/0obuo6xjITFyYMFwL1xV43H5MoBtAVC5Tqas7Wikg0mBxVAOMk1N0KUlqB9fDD93Hvn02n9CxY8fpK02cpikf//jHbGMEAe9413sA+PjHPsInH39cK+ewpQMzKAWFm/F2a4tHHn4EsKqo40dz4SEokkQ6rlN4y3wxyDfOxBgy7YSPPP4pVtdsed70tnfyec2NtLS8TFMTSdarFVJNXz/TqBUu3m5mo5wCzDabvHTyOACXzq0SaTCq9bU1+korr589Q6mkyf927KC/bQUhR6CrtgIf+egnOLzf2v/MqjfH9bDrwXeyferzAHTXL+DP2QnaWf0CewNd0Hc/SuapEYfrF/ZaYpJRBOYxpEnC+SNPAFDyMnYdtILTzsMP4y/ZSW8yigCDWdjDqOrKLVUoaXC6brvDsGUntJsY/Fnbd2maFYvyjZCNeWa57kiNZYyMVFpjAo+I5HHpcFwZJdsT4ex5G633z/7yg7TUpkIyoa95qcRxKBX2LSUidYtN05BUdfkXzkVF6AK35BXeN6XARfIgXMbg3EQU1GrDo9a0v226Ca5uZMbPSNXVenGPTxBoULYoKNyc1y/1KXVteQK/Qq+r+aHaFzm3rmrhOOOFl/LwAxElVZPNLrpUZzTa8K4ZBho1+sK5HmE79wZxMdj7e61N/MyO0+W5Mu3u5BtJ4HlkoQo56Siquut7o/mUZqTK6zveKIAkqeBmuiZFEHZsGVxxqOhzmo05anpACcNh4bX3wQ9/gOOn7FqyevEs/YFVNZcCn7Lm2yJ2C9WL62YYX9Xd8yX6TO7hc+rsRVp6sOsMIjpDFcDCsNgIS+VSobpwDQR5iIuZBntXrFddlhrqdVuXUm2GgT6nWpkZ2RRJqThwRGlSBL30HQqh3XVHuQejLGOgnlSu4xTBGGOTEQ8nMxOYqVWLjXWuWSbRAIcL9Vmeed7avm1t9ajkAUZ9n1TVd2VS7t5lNzLBo6NeV73ugFCFnMTNED28OcYwo3YyizWf1NGgleVy4Wbeanc4v6VB7aIhiW557VA4es6O/XTWY7F2/YB14xC8wvNr1646f+s77B4WnTrBv//tXwfgYnuVf/kum79uefchyronJemATIXAMN1ivqrCbLrBUG2cDr7RJfCtyn+2WWJh1h6YDy7v5dEHrH3q0Wef4OgRG2oi8zOMumY7ZCSauNNJtskyNQ1xSgyGk3sTLi5U6BZu9dvU67ZfkkgIE7ufDVJYVROTjc2MOFShNI3JDe2yNGHYH+V8IzsAwMrifWSZhoIgLQK+GmOQ3E7Nc4v57XtOcUA0aYro2mbcgDDJBa34soPv1TBVaU0xxRRTTDHFFK96XD/wYJoW+bGyNCVWqTYSQyZWcvd9h1ADeG232mwdt9Lcu9/8BvbstF4QH//4J3jyyScB+KvHHqOnJ/qvectbWN5pDTeffeZzHDlimZM4jovTVLPZZHvdSm2bGxvF6dr1PILcQyMKC+kvikLMhCkJroZcRfGRj3+SLxyxgdieP3qC3QetUeOl9a0i0/D+PSvEmlvK9xw8bc5nP/c5ApXoD+zbT1m9fb77v/lO3JK9Z3ZmHic31HIEX2nocinAz9kzxynijxhDEVels77GC889D8Cbvuarb1ineimgrVbwM8t3s/t1XwvAoLfB+rkXANj+1J/SmLGM3MLhR6moei4JGqRj7elreVZPHmP1BcvwrOxcKAwoB/0uRg33Ar9SGIYGXoCj9YrH8s9UavMM9XA1uNCikuRBLGNMMlmOotzQMkcRSwcHcUasTs7wOI7YXEQ59PsoivjIRy37uHrhQpGtubvdod/KPfCa1LU/7Xvtu4bhEC9/b0aR/ddlFO8j8L2RpR6Gm8mP1omG1DUekpiU1rptm3a7Q35uqVaDwiDSrzns2mv7sDwTcGHVntZ660Pyw3qv3ye8YNU65cAlUCqh3qizosb4Ti0m1THe2fDotHJGyyWrae4cfw9VNUJtn38aT0/g7e6QJJp8LlYcn8gdeS3Fee4zSXD1JN9crKO+E7i+Q97+0dDDS23dq16ZmuaTajbqNPN8XiYrHBRc32NTvRKfeOZpLm5YZs8rObhB3kdCkbfJFZv1GsCkOCZPD+FSDSZXoR87d7FwegiTjIGmN4jTpEgzUUrMKOy+cYqs3vV6vVAzbbR6uMo6D8MBrm9VC7v27KHft329unqKUNWsg0FYeLkkSYxHHmPFK+YoqTBU96nMzYr1yUGKTOs3wvxMCUcNq+dnqgz7ed6rgKo6MPSSLiVVzVQrFcqap2lhaZ6d6h0aJzEbm7Z/Nja26akDget5VNQ8Ytgf0m2p91MyxC3l2b3dQgWdhH0yjdcm4pKq4fnGeptA5/FCqUFp6cZMeQ4xHm7OMsqAGTVfeL7f5xlliA8c3MvOnXYOpXFCqu0XlCq4Ffve+aphXpT9oM/r36XehLtOkIRW5V+vHuAd7/k6AB4+OMOhA7afFxfO01iyc/fBgzUILHtWrXosLe8GoOksc/9r7P4qbsAJNT2ZBI7xWJixThjDUoib5etlQJLafuz33SJlxqW1i2S6P2UmwlNPLt91MIl6bDnQa9v2GfR3U1YDY88vF1oi8TKNMwc4TuEpHkZRET8OTLFPJ3FUpC9yHPDk+g4E13dL913CXJAYDNlSXZyTmiLnSaVRo9mwdNf25jr9jt3snvzUc3Q1qNbFCxcKF9wwHGKUNv38555mU1VXm602OTvtBR6eBv3zxBSeYpVKhfOqdy+Xy6ONzowSbnqOM3HAuqKeOQ3mOFxSz6//8tiH+PMP/FfA6hi/5dstfex5LiXRvFrJgJVFW/fDe5Zp6kQMKgFe2S64VUqooojFu8t0dOBX8KnqYCnvahIv2kFUdSqgaoC1bptgRgOQGcNQo+UeP/oFElUbTCLwdLY3KTetbVXAkKrq6SuNORrzliLt7TjB6otW4Gx/+k/YofmTmg9+E74Gl4rjIfnmevwLT7F62qoBlpbm6amdFV6Z0rLmfApMkUfKxYO2pWmjYEhJc874jkumnjNIRnfLun+7fp1skjxhQOCXi41sPA+b47iXCUPjAk9adLmDn3u7vHSK46dXtW0abK/Z8nZb2xDnYRUSyrpgVSpl2u089oJbBPkS1yEoqzeWF9CYsW3vl2rku7WIFEEuJ8Hy3r2EiR2bWxcieuv2vZfO9fCDPNJyUty/Y1fZJq4F/AAa83Y+rexYwRnaMfvM519gx5wdp299/cPUtMyfeepZXD1YDLIhrjLhvVZG+6IKsyUfr6mCVrNBb9VS2731Nr1ungBSGCaTq3uSKMTXQH+4Pj1dKP2yj6cHBQnKhXt+mkYMcunNcQoPkLlmnaV5DReRZoVXSblcoqx17Ay6nLtk15J+3C9S0tl+06Bpg6jIoVsuuYW7bDmoFbYxTgTlm4iksD0IiXOhfvy/aVLYj8VRSKgb847ZeQ5qENalpbnRuPUCLl20djuNWo05TdAYDweI2t81G3W21DZl7+4VNrbsHB2GcZ7TF4mzQuC380Oj+kYpJbU3IwN3wkCnD967wkwtXx8TwqHtz2iY0lR7zUa1w4J6ATaqPpmqgsNeh4tnbXkdRwrVWKPhMzujdn6eW9hrbiWC5tOl2x0Q5KFPUuirILm12aHbt+Oomzp0VQAfmAx13sIhYa45edRzB6cI8Dk349Mo2X4LTcw9b3qjLbMfIDqoSq5HVUN4eN2M177uYQCS3lle+uTTAHztmx/ia15rBaRq5RLdjn3+gX3zLKn7eRb26LZt+9xzz2EeflRtQ7vneexj9gC8Z2WBukZA/6a3fSNveJ31gjZumZOnLk5cx7sOvpF5NX3w/Qr1mt3/DD5fOGrnzcefPkqaH879Eq4GAzSxFOqtTAwl3zZ0rRwU9qndTo/GrAqNboYxowTLudloFIVF5O8sCdlat8J7t91iR9Ou2W97w2uY1UjaqYkL77BrYarSmmKKKaaYYoopXvW47tkk8WyGXgAnzYpYG44rhWopjWLueY2l4vbcU+ZzTx0D4MzaNuHzJwE4eO9BmjNWSm1tb7Cugds21td57jnLKnhBmUZTPQTShJ4aOgVBgK80faNeL07yWZaRZSMPmdwaPcuyy1QwE0FPOJmhoG5LlSo7d1k6cHFxgQfusyqt/fsPMKcZz6tBqQi02KzXqWvYcpMkVDXVQhINWfvsXwOwdvIpmjHkJAAAIABJREFUenN5IDChe9waJL/23e/k8Ne+k7wQeVbhhZkaiVL5hCl9Dbt/7MXj7Dk4eY6iud2HSDcscxFuXiTNM9BGPTxVG8zsuZvmbpsCobV2geOf+Utbx796Pwdeb1Vg5b0P0cszhT//NGjusFY/Zl4pxihJigBaxpjCsn4w7BCtfg6A2A1wVeqPygsMNZ1EpQp9TTUy2LiIU52UZh6dTF3XK7xmrNfVKNvuyOjUJdFTsEEQjf1x5PjJ4vQdGCHVU2KaJji5V8ugV3hj1es1traC4vm5KtLz/YKFaNbr7NxtT25BuUKajJio3Gh2EgwGA2INrDfsO4V62fPKBaOytdajrClNamXhdGj7qtcb4Cizce+9h+ipl96OxV3sWlbDQTfg9Jq9v1wtcfdrNCbIzvvpq7dl/9IlXFU59Xpd+pdscL/qWovZGdtWM7MVNtfsiToolanNTxawDsApZyP628TU67ZsXsUv1EnDQZ94oHRM5uBqGohSuURVNKjgwm727rBtvn/PXSzusPPY99wiINr5C+d49gX1bHFhXr0hM5Ph6Mk8TAd4usaQGkTHUrMxi6cB1NJkSHdjc+I6ppmxqk0si5GpqmDvrpWCmV7b3KCiap49u3aysmz7ojk/U+QMjJOsiCm0vLiTncu2jkk6QDS6XhRGvPD857VxhdVLdr05dfrCKCCnoRjbsZMV8ax21OsEFVuGXj8kSidj6nbuWmZ5SXPmRYNCldNebzHb0Hg7VY9U09KcOrZWnOIRB1fZ2Urg4+uYdX2PkqZ2qTWq5Of09c2tgpGPjENF50ejklprb8BkCWVt70ajwpLOuXMbbSqar+yRe/dz36GlieoH4IiTpzrjkQcO0dRUC29606PMrti9oWF8qoGql4MqwyjPoi4s6VirGOGr974PgJV6wvOf/jAAqTzDrhXb3nt3L7J2wcZlG9Dg8EGbz81zDFurNp5ad/Mily7Zcf3IQ/dxsWPnZRxt8QlNn7K06xB/+udWXf+GH/mHN6zj177j23GD3HykRM56hqlDy7V5u/YMFjGbtpzu0jZhaE1Vwu1tQmUTe50NMmPXS3EdEjUwHsZpoap1Tb/4HIUhHd1TO90WPfUObG+s0VVTGE8yDh+w87vdewuVujpWReHLzBuuxHUFHs8ZuVyWaxVS3VTSLCu8X8qex8lTJ+0P3ACjlTt9aY2Tl6zOsJOmvO4+u5k2qx4ldWGO45DzF61Kq9OPiLORvjzfVCq1GqNccWN2D0KR0CzJnCLYWWZG3giTIg82t7G5xZNP2k35tY88zDve/nYA/MCnqslMd+7aSUXtcyQzJEqp18planqPE2U4uiC2oz7hhh2Yi+kmpYpdsI622/T7tpM3LlxiTt3VdzVqtEKru+6m20USwXYr4dw5FQJnZhlkk2+WQbXB9hn1SDBDRIVDNxvlCJMoKvp6ZmGF13793wbg+DOf5PnHHwPgUL/NZmIn8bFjR6mpnrbX7xe5UNxKE0eF4SSJCluEbrf9/7P3prGWZdmZ0Lf3me/85jHmyIjIyKgcKgdnlWtwVdsuT+1Jjdtqd4OQQQgk2hK0EAIBkgEJwZ9GSLQFP2iVBRLd7m6rBeWpBttV5arKyjEiMiMz5uHFizfe+Z757M2Ptc6+L4tyxE3RQhA660fmzZvnnXv2PntYe33r+xY8dggarXkwUQFhbw/5mELzXnOEjGt1jXtj5OP+bO1zXUNVtY6oLkspTZuOsrcs2zICZ1LaGHNdqgf3t+CWjnwUoygXKT2tsRUnCYZDVsSt1cz9tdZASTkXwkAAnWYbS1z8Vtq2Ef86+myzWJ4WkMyE2jy5jIKZODtbE/SZhbK/00duDgQWtne4btf+EPNMwd1rj1BEtFFq+Dhkyu7b775l8o4unFjARodgFFlv4ZtvkWPQ2+1iOKJ5ubjWRGuODwpxD5NJqUouDWdp3ItgDWefi36nhVGXC9JaEk6NN7m2a4QiVW6ZGj81r4ZkQvePuxFOH6fw/aUz57GxTA7A2vo6fM4dCaMMvRFBpt/9/rdx8w6JZdqebWQKsiiFVyqFuzYKhkBEptGqUVi/7tVhGwVvB1lvdur9mVOnkLMD+alLl0yB11MnT6LPsPC//NrXsMhwwtLyKnxeM7rdARqsUD7Xqhkmkue46DTpGiEcDEacUznqG9G3PI3NNQc1HxNOMfB9D3XODcuLFGuLtBmvzy3AZdFC1++gP5qNll6kKYqszG/KYXPOX6tTx6XzlFty+fqWybGRQhvYUClt8ozGkwgFK/1qrVHmO1iub9SV4zRBzvMyUkCc01qcZyn4Z2EJC0123J6/dBrPX6J96N72Id7lA/nxzWU8c/7kTO0DiAlV47VvdamFpTqN/TibQAqaT5fOX4BVCiR2D+Gy83Du7DqW5+kg13E9qB0W8L3xEX7wPZLBKOwdPPcivf8w2IcYc/20+ZPIOPdQZhnSfRpH48MIcUR7z8aJJmo8d7sHjwBNz/Pd717Bt79PsNd/OYPDI4RrCiDHaYRC0QFrHAs82OY6aJOJEVj1PQnfI+i+HxXIHbreD+qwVEnhL5AkI+6TXSQpvd/DgwPD4u4PBhizJE2aJHC5vZbOcf4MMZM311Zwkh0erQsMeD1O4tis0xf+mnZVkFZllVVWWWWVVfbU2+N1eIQFq0yky3ID9xRKmcTQufl57HOdobTIEcalNscIghOI7rzzLib3COqqzQco+HivYE9P3bpAwiftzlwbtfq0UnJeJj7qKSNHAFDMPFJQsHyuTl1zEPifzI9LOIn6nXcuo9kk7/vkqROmXEWeKwz59OU7NjR7vloJLC+QVysFkLNwWJ4mKFz+7LnwNkmjZuugh7t36HuvcwEbDI2tbqzD1hTpunr5j9E9JMjP8hQ0J/eun3gBp54lsUF34dOYjB5fFfZjVuSIDimcLaxplKxQOUSZ1S6P1E5TuUkUP/Gp17DXJrGoe9f/Cjv3iLnm6zGiMQ2f8WSIIUu8r7RWkDEsJXQOzRGwSW8AxfoNKrGhOAISJhnQpcx9q9DImvRbibYQDWc7VU5FsT4e7VFKTcfLkWRmIaXRdVGQePNNKhsx6I/MeB91u1B8Cj2arlkU6mOV2Y9Gk0oGje97hpnXajTQ5MRzSAGXw9y2bT8x/HrUVo8tmRISaZagd5/eZ3upDp+h4PZSA2UadOAGGOd00m+2UrRYwycMu/AEnfTjJMWQS3w0a3V4DBvlWYQJwzTOqIusS2Pf8WpoyLJ+UwzNyduZUDigJQDDSQHXY1ZUNMBkMnudKdcJ0GlPIULJ0U2VKSRcB6rQAq5Hnz3pIo8oArMxv47Xnv80AOD05knMzdG89AIXOYcZx2GE67coqnPl/TeRK4qYBl4NFmdKFkIj5yRarRVkWaNNSBOtcOs2ctZf0nlm2Iez2G/+rV/Dtfd+CABoNXy4DRrvF8+dw9WP6AR+/PgJrK0wDCodtJcoQpgfCiME2qjVkUSc7JtGuPfgLgBgNDhAnLJ+lBRl0BH1oI7TJ4igEI7HmD9HkY5ms4len07UNWnjFLOktNBY3KSkWNtp48H2g5naNxlOEHsMCyOHYhg5TQq8/Bzd79tv3cGHH1KSe2DbZk5AwcDIeUoQCABY0EY3yK35WDtG0buLz51GvUFj/9337+Gty1QyJQkjyKwUPAVqnFT8wnPH8Is/TTpuOwcDOBz5ufzBDXz5Ionhrs/QxjQZYnmJozTNACKhSMXk4CE8rk3X8gTA8Knj+EaIVhUh6i5Hsfo95AOuV5UJNAJKtM+kj26XI6PLGY6tcUJ1LcS1LYIod668j2tfo8j77t4tOGdp5qf2hzj/KXq3vd1D9Hnu9g8nEHp2NuFkEpuaXMJ2zD5x59YDfP2P6Hev3b6GjNvruXW4Fu1nSaLQbDKj1C4w4fFlK2H0i+7fvobJhJ4tjCKErE3lOI7RD5PSxdIywWdCFfjpL5Pe0U9+9nXIst6WACJeY2h5f3xy/WNnahwnsJmNUBSFgT80poJyezu7JhdBOjYCVpQ8fqINFoNFLdOIOYM70QUk19+wbAsbGxTmtLRGViZ82BIL89R5w2EfWVxSACVqTDedTELkTO+0LYEa0xxbHRud1uwvFgAODmhxf++99/DKa69TG7XA4SFBKlIKk/19cNA11DcL0zo6rm0h4AUxtTXigl5y3e6g9dxr1Fd2G/IBwTcbq4tYZRE0z86QC9r0c3sCr8H5HzUfda+sM9NEzmyi9vIiMjEb3AMAChrc5ZSzY8bEUTXjKfSgtEaRlk6dwtwqvSMtP4/7DF8+d7KFnR47txlgNyjPQNY6SBPGbCGgmW1i2RaSjEPRYYyMgY8wCuGywN9eXGA8RwtrEcwbocInmW3bRmztxo0bhu2yurpiis1+zOEREjCifId4602STMjzAjGrxMbjiYFJhWUbxyZTBVKmX6ZuiowXaCEECpTUUw8uw4adVhsNpvjbjg1XTGUVrE8CaekMg/6Yn9OFxZTwmhMjYPjUGys4/B5H+yFKcGlpdQEe49x+YOOZE1TgcHdviMmAxp20dekDwnPqKHgh29npYnGVNsHdwxBDLpDbmPPQ69OGW/MD2BZtAM3AQ8qQrB84CGruzG3UKjUMOCltZMzciIcTsCAtCuGg8Jh9lmVY9GlB/Ne+8uv49MWL9Lew4bNIKvwApdxaISLs7N8FACgdIwg4vyuPkKd8T8tFzI5EnmbwWF3ZrwWwuJxUFiSQTL0PD5Np3bkZLBtPMNehG9UDDwvLi/xwOXKWc5hvteCVlVwLicUlkotw600Mu8S0kZac1p0aDjGacC5LHJkcxlarBgUaqysbJxAwXf3V587iZ7/8UwCAoNbGt79P4/9bf/49NFlItT2/iA7TqpNMI9u6P1P7wnGGic9zKEtMvuCkkOgwVPjzn7mIQZeepd/tY8AyH2vLc1hepPfm+R46LCewutDGsWPk9J04tYa1VVo3a4EwENhrz5/AP2bh17/4zrsGLnYlsMxMxOXlOXTm6Bm8wMErl04CAP7kwQOkxeyifKPxLgIWh0Ve4HCPvP1kNMBxhgQXWwG8sjJB5sNn8dwoHmDnPjm2dp7hxfN0GF70zuPzX6D8nN4kw//+h79Hz1kUCBjaHSmNuw/pt268fxM//A69N4URnjlBDpvQBQoWB93b3sX+I1pbA38BSTQ7S6vRCIz8g3BcRBHd84MP3sPwkFJVWnaKouCJmaeGlt6uz2F1ifpn91EPo4z2g1hpCA52hOMenmMn86d/+qdx9epVAMDNmzdx9x7tAWmioVn+4ewzZ+HwnNjrjszaaUsJi51q3/efmCZQQVqVVVZZZZVVVtlTb4/X4VEZ6pygq7REwhXPszyHVwqrWZap5VMkCZRJKs6Rc0jS9gLYzOgJYAEsRpXZ0lTTRa7MiTrLUiQReY6eM9U3SZIUObM4XFeacHM98GFJ8hznWi1TS2ZWK8tY/P5Xv4r7W6Qx8G/+9r+FmCGqj2m5SGGYDK7t4Pp1Cs3+4T//A/zm3/4NAMBLr7wI1Wd4LlUYuKwxsFHHKU5283zAa3NUymsjcOhks76+Bq+E+XIFwYlvYVFDP2fZ9TRBomZnhghpw27SSVKnoyP1sQRKGpVWU1G2Ii8MvJUXuWFRBI021i9RIvebf3obnXm6/vT5F7G6eZauz2Kiu4HC4kbwo9GBZoE2LaQ5zSZSTKMt4xQypZNkURtgMGOlbct2oDh57jvf/yE+/IjEFE+e2MSJDTodv/T8Czi2SZ8txzZlUu7ee4DRkEOu0jJtTXUBDlYSQ0iUzDZtyoAUeWZELh3LpnovAGwL8HjMnDl3Fm3W0cjFVBjLth2j5TGL3bt8x0SxguY8Why56h7uQLBo1+hwYNgjSZwi5urbxzfnsblMzzDf3ITNofP5xUUkQ0qodxwFwTL0aabw4MEe33+CBkczJnuH6Coas+7cPCacYHwQ7sL2ywrKBQ736CS5uNIwCeGz2CSJkfBcWV5ehuSTalEozC1Re51GByknp+pxjC9+4ScBAK++8ip8u4T8NBQnnwttIeaI40c3P8CV9wm+zPIYHrN3CgXEJRtLTxmoC0tzEByRm0QRUta+SgobnuA1xpHwncbMbew92ofg9enFVz8Dh5Ox33//I2QcWXIKhd4BRYLnOgvY2iJCw8LyCuKIxuf123ewuUrjuTPfwpATld3AhuBn6w/HGLJgX5grbCxQ9OEXXn4Zy8wotWoufvlnvgwAGIcZbjGstn5iE9Kndu3cug8vmC1qPhqPsNDhRP7MQqTod2K7ZuoySimMwlZ/kpgk9NdeOodf+CJp1KyutODVqR3SBgpe96UQcN2yFpULwVGdleU2LpwhQOrNtz+Ay9CrLzXiMfXZB1dv4TOvEtu24Vr43AsEsV067sOezC4C+mu//hV8+lOUQFvzavAD+tsLC4vIeQJmSYpc0+8O4gwFIxy3b11BTdG6tths48zPEUwzV/Ngcbsebg/gtShinkIYkkRYxMgjGhduW+FTn6XU3Jrrwlqh6MeFc+dw2KWxMDgcYHuL5vHdR9dROLNXhPd8AVFCykIhZthOiBjH1uid7j1ykKWc8K4l/Da1S9ou8oj2VFukWFpgLTnLxiJHwO7dtRBGtGZ856/exKBPUeH+IILiNUYLjUOu/ybv7+DRATGHHccx5WI8Z7qOtlotrDGz+qf+xmd/bLseu+J2OjUofmmNWoBGgx6ke3iAtbUV7hgHmjO49/eHpnaO5UhoDi/NL7QQsUOycphAMp75yC5wyKJQtBGU2dwWInZ4VleXzYTY29tFh2munuuizrk6vheg2yUHwHUF5jqfDNLa58VlPJngwQMKpz3a3YftUKcKIY4UrBOw2ANr1gL82TcIz/zHv//7yHnRPHPuDBouwxgygGtTW06uLyDYoMFycLBvKP+QApZk1VprCXFJXYaCZkXPqNBIua5PlkwMjW8W01BQJbR0tBionuZiFUVh2pjnucmbyoscGf8ttDZQweFAwOINxrPGkAPqt8T1p2wlPYWRVKFMwcVCpUi43kuGFLU6Q02Wi5zr94y7A4TubErL0rZNjaSv/MIvwavTM165+g7efIPUoP/ym9/GK6+8DAA4e+E8Nk7SgvVg64FxchzHhctqrdoWyLk/XGlB8sLqCBcohePywtD6G40GrDJ3TBdG3ffsxQvwglKuIIUs5RMgTF7CLBYPM2RcZyoZdNFTNGajSWg25VE/Bniendmcx6+/QjlfX/rMs1hj4bbf+xfX8NWv/yP626SPjTnqK6UyjDmfwNYCZZ3BOCogc2rX8VYAL2Lq9NYeMs51GfQTjBPO8cgJ6gWAg/0RonR24UE3FkY0b7LXx7FlekdefRmPWAk517HJD3j5tdfxhZc+R8+vbaRlvpbjQNpMabc8HDykZ3vjrTcw5nUFQsDiflOqAJj5I6RFYqcAsiiB4HwLy7URMYTnZA4UrwGu6yOJZxunAJBNIhx0yxpRYzhMIc/SFHV2hFwJ7O5Tez91/BjCHuVr5baDFc5puHXvDrwabYTLG+ewyXUL676Ld94hpmmBac2sUXeMvEX5Qg+2DxA0WAwOFiyLxsznPn0BgTrk+8S4eoec4Uc7PdjObGtqnsUoa5RO0gDBEjkhi+vruPrmdwEA79zaxZBZOUuLHaN6PhyPMTykcb0YZHA0vWen1YLboI1SWA4UQ52ZygwLLfAFjq3RGrAw10TMordrawsY7NM9P7qzjW98l3JgvvTZi6jz9FtbXsFwe/Z3uLLahJS0zy3NnUSDgwKuK5GZOmZTwVxPumh3aD3Q+Rh7DwkSylstHPTp8/ajFKOQ9rD72z2E7BKePH0CsqBxuhIUQIvrwp25hNUv0nomIwfv3SSn+P3Ll3H1Kh34Hty9i9GA1rYEPlprsx0gASDJEiOz4foBXD70PHx4C1ssOPv8cy/BZch3t9tFyjksWaFQsFBrlk4MxFrkGXp9gl4zbePeQxprN+7smHKNnuehxiK5QlgQPH7HUY4xsxstyzYCrlLYRs7Bko/gXKMc0/8G/+DHtquCtCqrrLLKKqussqfeHhvhSbMUMcNYjboPm1kcq6tLpN8NYG+njzonRAaBjVyxWJTrIWQmkU5zHONaJeu7+zjgsLUSKWp1ul6lgMPQRpLEmEzobw8PDrC6ytnr6QTdPYaHXA+NE5TINhwOcPcuiS2l6TJcZ/ZTJQC88w7Je58+fRoLzLoajScQfPKRljTRDwEBDjghz1PcZP2cZruF67eIiXb7/j2cufAcAOAgTWFxfY7u9gh7zEhaqrdQquiPvRDK4hOG7aIs5V1ojYijK0kUIebqtb3R2NQSmcW0VtAMO9oqAYqpoF4ZgcnzwkQ6siw/8jlFxmwDDY0Jl35YWmzjzEuUZLc73sXhvT8HAGy+qFFboFPoJLOMTgZUYZ65KApkHOEREvBKPZ+8QC8tE0kLFPlopvYFgY+ETxQnT57Ab6z+bQDA5774eVx+iyCMv/jGN/G1b5EAZOPtt01SfJ4XyEpWFzIjQum4DhRHH4sih2JIy3YceP40EdfUZrJtUwspiydY5NDq/OqKgXk9PzC54UJIOM7s0KsTuMg5eb+/PzAS89KykJYaSEWGn32ZGBr/+s+9hovMZqkHAopPaMn4EHssGNhqNnD7LkGyaRbB4+Rb33VMaZHcVuiO6T0cm/Ox1qBnqNUEtvbLyGthqnsLYZk+yRIY/ZFZbHlxDoXDxIgU+OyLRCDY3+ni8BFFnzaWj+OLn/8pAMD5k+fQ36ET496DHjZPUTRhaXUZsEq4SuP6/Q8AAIfxFuqLFKmIw3QqAqkKWF5ZgduCy0ngWZQiZTZWrV1HnauT61gbccK0yD+R6lcUR3jhPOkF1VWG2zfpXeRZDsXsxvX1FRT8/F4U49I6RbqaF55FxJHMF37iM7B5nZDKwsYqRW+ycIT5OSIZvPL659FiFtPXv/UdbHNy7crzz8NiIb8kCjHhKEOcxJivlxpaExww9e7hwx1YM45V2wG6rKNyME6w7NM9avshZE6hn9tbD9Hm5OEvvPYi7j0kCHQUx4hFuX/407Upy6F5DRI6NzC5yqd9ZkFglcvzrK0u4f0b1KbVE5t4/XVKZv/6X76JP/zjywCAcVjgb/38SwCAyVghTih6tzhDG7cf3UHDpYiTfeIFhLznxVGKZMC1y/IYkjHxRAQo8zIaNQvRiNrbWKvjh29T1MurddDmEgkbZ9dx5vm/CwBwllaw7jB0KSwkPJ/u9nZw+ftvAACufPsHeOddWuceHfQwGjET2JY4dZKSoiM4CMXse0YUp4g4cinGMTSvkesrK7hyhSJIqcowHND73dvfMVDacDJBymkNk3GEWo2jW0pi74CildJyIG2aizW3ZmoeWnIa1YFwpixYIY3wJwDD5HJtF3UeywudOtZX5h7brsc6PEmSw+WBnuc5Aq4P1WzW8MEHFDrqdxMsrtAkDOq2CXOHkwwDpiq7tsA5rp1Si2PscA0pa3Uey1x/avvhI7AGF8IoRMQZ316aI2XaeBwnZqEZ74YYDDg/QGj0epxHpEdIZ0+4x2g8wY2btwAA5559DuubRN0cjEPML04ZPqU4oRACNsMeURZhzLj78y++AJcdwnffehurrFq8NwiNmuruzgi7+ywqOBdjnjeVoOZgzAu9tJQJQwshUKipgnHJYsvSFP2d/ZnbWOQ5xn0ajLLTgFJTJ6eEmfI8N4yjNE0NjJVnCTKux5OrApMuKTY/c3oDcyu0PMStOrq87O/cfwdnJGXfJ9YGUmYKSVWY++d5jiyne0bJGI7mGj8qh2Cl4E5eAKNZ8z+EybPKtYbDlPCzZ5/B2VOE07/04kv4wVtvAgCu3fgQh7u0IEZRjIBzFQqlkLFTZls2MsmOYV6AyQLIiwKc+gHP84wTl+YZLJZJEEpicYWcPq/VguCcEM91oIpSeNCB9wmKTka9EBHPJ601Cv6t0Whi6gD927/6Gfza65SjIKIQXVY013Nt1FbIwfs3fv55JD0uuCg1PnhAjfnO1btmA+2PxugNGTqWNkpvJqxZWDhOcz0ajaDukhNSFw5CWTI4pUkRU0qhtbAwcxsVAJ+xeW1r7Hcpn25xZRWv1ohyXvNr2HtEUMsH77wHwQKcJ9ZOYYHna7HQhmNRn0yyGHce0kFkGB4iYBG/XANhSHO3026izuvTYG+C0Yi+b3oBasyQ8WwHNYsZW34ATsnAeDxGzGveLNZaaOP0CvWJM+gi5fwxuB5chrRUkWN9iTbUg14fxzfJkTu2soxFhoicRgMB51EKSIx547l1+wYsdlyXV0+gzYzPjY011HnjCdwmonIjCVz0R7S+Rk4NDhcQbtV8XLhAsPz3f/AepD0bGJD7bcx3qO8bSy6Kgu4dhgX2htRpmVI4d57Wx5MnFlCweveb713H9R0am8dPrWIpKJ0sBXDOHzIJwY6QBQ3B0HuRK7SYfn72xDo+ulfKcCh8/ido0/dsG//yWwRp3b23iwkLRlqOhMpn3zREJuBbNF6SOEHBcLcNDdujMdIIAgh2oj3h4jOffhEAcHG9hhfPs3L2whxylAzOOiTPoThPECfkHG7vDDEe3aXvtw5we5ecpSsPbuHGdaLhj3d2kES0hhZH2H4i0bj+ITn7tZWTcNqzH7CKXJu0hrJOJQD8zM98BecuUJ7V3Xv3sM21LRfmO2gxwy8rCqNYD2EZ1qxtOfAY/tNH0kSKooBVVnEQwhxeU2UjL8qUi9www2v1OtZ57zy9sYDNDTrYtVsN1OuPX1MrSKuyyiqrrLLKKnvq7bERns2NeQw5PKlUgoQ9PivJ0O6Qp1YogXabE8qkQp8ZFFGYoIyCCpnhFMuXu+sLqPNJv728AcFem73hoh+Sd58LH5L/NtUxdg5ZwKnmmbpaygKirExABAJO5oqKEGE8e/2eLDPqPzjYP8DF58kTV0Vu6u44jmPYL1oX5nQZuzzyAAAgAElEQVQ9mYSos1Dhi5eexbUrBI1duXwFX/jizwIARt0+ehyp6PYmmPApfU/1MA7pJGbbjqmcLaWALqa1cELWzhgMe+hyyLC7v49dZpP9/d/+O09sY5HnGO1yFXC5aaIYRZ4bkcAiS02F5ixJkKb0HtM0RVZ+zjNsbd0FAByfb2I0pFOUAtDZZLGr/S3cuUVJk8sngbFimf4iM9GQPM2QMMQ2GvURjqldXttHs8ZwglUgLWZMWpYCDkcnbGEjL8qqyRlsPsm++MJzuPQSwYwP93Zw7zZBOVcuX8X7V4mZEoYRXB54UipoDulmR8QGC62nulO2ZSqVR1EEj3WhGvUmTpwl1podBNCyrNUm4Njl/W24MyaCAkA4iEwpFaU1NJ9VGr6H3/ll0nn6uZdOoHv/Lj/PAMdPU7SyfmoTklk289sP8dI6zaGNpQCnOZHxvdu7iDny2htOoAtmWrWasHjwRykQctLy7laMgpOoPWmhYMgs1VQZGwC0AkaHs8GSANAMmsjUdFxc+YgSzleW15DyqS+aJAiHNO8XO0s4eYzgIbsG5BxNGPWHmFvkBE2h0OAk56WlJXisn5JECkXKUQABM360KlBjGGihPY8ooiiAk0ss1LhqdSqQy4KfuYbAnf3c6NQCXLlOEaeldhuTpEzYBzSH6Xf3eqhz4v3pi5eQMFEgtmz4Bc1FFabQHMlxLIF4RG2Jh/u4/4jW0YW1U1hdpLbEUYwRJ4x2u31kfGqvd+ah6xRxmK8FWFqnMZMmCVaiuwCAjZUVQMw2F79zZR8BkxCUNR2nWRLjo48o4phoH7Gm53rn3hi9EfXlYVTg3VsUqRJ+HSdWWIenU8Niq6y47ZhIumMdqZWHHCUNzHElfE6EbloWfIbwL6x3cP80tfXB3iEGzLZcnvdRD2bfMy498xIunKKowsL8HGx/Wmm9KNdWVSBmkT2pNc5xFCJuBAYp2TlMTCS939vH3h6lO4TJCBnroEXjBBFDVOnBIfaYYNMNh0iYvZVnBcZMFjoY7CEPuc5erpFxcm9eW0GnMTsrtN8fGiTAsiRK8TbHdvDMGZpzZ06fPpISkU8FXwuNgmtmaa3M3woISGZuQyhkZSX0ojBwldbT8iJK4eNEmqJMUPeNmCR0gpCZoP3BAT68Qf3zlV/+pR/brsf2wIVnVpBkJDrW7qxgZY3CnUprWOx4SOFCa2a2AAg59ybwAjTmuBhoNETtr6gez16QYfMkDeRekmDnEUELC/Nz2Fhf4vt3DAU3TVMoDo816nUY+gi0cVSU1ibcJYSELWd/sb7vY2OdwsT1VoTbNwiqe/e993Di5EkAwMbGBhpNCj3WazW0WCE5ThJTP2ttdRU3rlE/XH3/KnoDWnT8wMXeTbrnw61HGHFRtTjqHylC2TBYZZIkKEqGVJYbbDlJInO9LSS+8NkfT7v7cZYOeih48mF+2VD+syyewkxZipRD81kcG7ZBkY2MEnWcJWgynXWMAl5CrIKiAKKoLLrq4yBk+uPWu2jN0+Q4zF3jiKRZgqRsVzzBfJsWjFRK3N+nZ7MBxHq2XCxp2ShKHEVos0FLKU0IsyhSOAwhHd/cxHEeyy+/+Cr+yR/8AQDgT/7063B4oXRcb1pTSYUoi4IJKafjTinU+BoBCc3jNKg10GRxOSkd6DIvyHFgl0WbtDC49Sy2tLmAnW3a1LRdBySNo79xcQ2fXqd38uHlD9BjuOriiyew9iqFnoPNU8iYxfj+tQe4/4Duc3HzAn7xNdrgvnl5G3/05i3TbxE7M5Mox8opmk/Lz3YwPKB3ko1yOLzoSORo2fQMh3GGIitzogSycHZaeqwjeCw8uNhYNkqvD/u7GPdYjsLy4TEN3/EDHHB7w9GHWJ4n/L4RtMyhQUhAsdhZlmfQmsP9cCG5/+MkMbkgjZaFZEif9/e7QEb38Rse6nOsLO0CKTMUpeMiwOybpe16GDPDsrs7xJilA4pe3xScXTt+FkOGmVItkDOtfjDu4YCFIossxphFIIsshcPjv3fYM/mPD7ceImZocnFuDWtrBO8uHjuOR1sEC+7tH6JdrmfhxKwHhZpCtz/5+vPgPfqJ1ksEMm6HSDNYXPw4jQvYDHtvrrewsVi+Q2kOsQ/rLoZMVb69M0bCOQ5xrDGZ0HPVPImAYe9WzTP5G5Zlw2M4yXVdrLMMwxdfeRY1Zo2uz9fw+ouUD3X3j3fx6ICZwB3PHPBmsXffuQqf5Sxv376HhOd9kmSYTMiJCsMQMTszSiu4jIk7elpnD675BMeyjRTIRCQw6rBCw/ZoDIYoEEf0bncebuPBDu2d40HfHM6VNWWO2jZM7mGSJlMl/RlMa2nWURJtpe/TNEWW0zMURW4Of5ZlmbQC27aA0oGBNgc1rRUUSztIaDhuKePhGvFAKQQ0P6etlVl301SaMXB4uIU7XUoNedgb4O4dyoPb2noImw+U/8l//p/92HZVkFZllVVWWWWVVfbU2+OrpSOEV6fIxqsvv4qTZ+nEuLfXR+krzc214fMp13V9RByFmIwHSLiOUm97Gw8mnFS8uAC3Qd83rAQbfDr1fY26Rd9bULB8rugtPUh+TK21SaDUUBDlafmICQgIof5v3/91JqVlEnfv3rllanf0D/Zw9V1Kcj179hkEHMlxHQetOTq9+42GqQ0ShSE+//nPAwCuXL6Mf/Q/kdZJZ3ERB5yZHg6GSPn0lSaRCdEd1fmJ4xhxzAJncXwkYTtCUKMTzH/9u7+Lv/d3ngxllTYZdo03HcyvIuNTQpKFpiZQnqXIONktTUaYlOy8pAurTFqejLDU4HILIoWtKXyY2S6irBTcCmGzxsdBd4Ral7xvv7mGg6QMT2aG+ZWrzNSgshQQ5WX5B2X0fJ5ktWYLMUekhBBH5MW1qY11tO4Vcg2puGSH38BzF4jF8eGH1zFkRpJWCknMbEIdw+ETWrPRRsCaNs899ynUOAn2+o2b6HK9sp1uD//0n/5zAMCZs+dw4sRJ0yaLQ7pSSFjO7OcNv+0Bu1zbLStw8SSdYC+dbOMWw5t1x8W5F+kUf+blZ6FtmruTezt4cIfghL+8umuSCAvbwXKHTte/8hNn8J0r9K6yvEDCyfKFrbCwSfcJexNsXSX40ZYFPGaVCO0j4qrlIlEGns0BFJj9VFlohYhF4hqNBpyA3lHu5ljkRN8izSC4Tkqzs4SMhfh2u3vY57pgZ04+B8HvK80iDEcMlacZbI4g5So1ETadaehSeLCQpjab7zeQc7THc+oG/oVlwykrlUsH2ScQV7R9Dy5HY3YOtzEJmQ2Z9NDhZN/nTm0i5yjc9Vt34HJUNcxi1DyudxZH6O5Tu9zAxdI8RcddvwHbLeX7+7j4DEGr8/MrmFte5j6ZAJKiA6NJDz6X/9CFNjWTonCEKGSm03oLQW3GaGQxhsPEkqW6gwYnfce+h2VJ7/DVF45jbZ7GlHMkSdUPx/hom2A3nSYYDVn7JYkRjqi/m00PdX6HoyhHg/cJz9K4t02n/vv3t3FmkwgVKx2JgiPXUko069R/Qc3BB9cJtr20PodwNDv0+tWvfhXf+hN6fqULUz4jTTMT5fA8D+0Owarz8/NYaEzFBstyQe3adKzJYlpnb15aGAzoeaIwxc42Pee1a7dxg+fx9sEekiOQmSi5gtZUzFdBM6QEROGYqNCzmhbI0jIyA7N2Cqlh8xrmOp6BogRgKpVrdSSqA1UGe2BJmD3bFtJEtwqlkHLEP81STNhXGAxD7O6yAGr3cBq53H6I+48o0ukENTR4b0ah4Nll/P3H22MdnpoLw5rZebSH+4++DQAYjRIkzKKyXYUJ03dffvkzuH2H8Ok33/o22pxlH+3vo8HhsYXaAgpm6PiORJuL5ylVGOhKaY08KyEqYRSYtYKpraE1KfYCxJwpcT8ImPDbLGbZFlZWaCF49Cd/akJeo/4h+kzL/CCaGDyzKHLj4NWaLZx7hlgxc3Md3L1NkED38BB/8U0SJOwszmPCm2gyHkNFLEwlhYlaqkJ9bKKUUgBhFJmQocpTrK8Q/vyZn3gN9caMMWYA4d4jWJwPk6UJsqJke0XI2OHJsgQJ5wHosAcrKVVlY5PDU8QR+ryBWcghmWpZqxcI7JJOLiHK2k4LLez0aINcdfqoKdogD7LYQJDAFO+VljZF4RjFnal9Qa1mJp5lWWZy5nl+pH7WVARRqwKOV2LJAq++SvTUk6ePYcSsu6wArrBcweW33sGQi2l+9gs/iWdfoFpUz164iD7XAfvv/+H/gMFdes+XnruIF16gIoW1oDalD0vL1KuyLAuOMzv06vouTlwi+Cl+tIfXWHm4cB3kvIDW2i4W1mmhrzcXceUjgi16+128+UNia1x5sI8vX6R8AqULs1lvNH2cXiZI6KOtfZN/kNsZbr1PG2t/L0WtRX27eLyG7gEvTP0M0KxynE6x+aLQRlBsJksl4j5DrA/7Zu0RbR95nR0PH2j5lNPSas1DcL5IlPex3WMHXNgok+62HtxF/4DGoFXAwMJurW4KT4qJQJ9rDmkFdOZZANCxUdhcU84SprZbFMUo2GH3bB9xOHuB1EIrDNgxS+MRarzhhZnG4iI5LZdeeBanThDc8vZfvIMfvks04Gw8wuc/R2NVFy24FhdYti2jnp7nBdpNGhtpODK1z+LoERRD/blO8CErKrueixHPhSQuEPL4T5M+xkPaaJudJixvNthuba6DuRaNnVfPrqA7pP7+1o37WGkx5VwVBjIXNkyeT82zUGPG1ukF10gjTMIM4aSsz5ViyDDWgc4h0hI20rj5gJylg94Er16kuaKTEGmpB6o1HF6/fMfGG+zgv35+BZ18dhHQKI5x4yb1jZDCwOn6yMZj2zalYABYXlyE3qC6ZHNuzbCxVH+EhJ2HKIqQMGtw3e+ge+suAGB7exd3uLbU1XGEbinipwvjIHmWhZTHYwZAl4c8TNlPhc6NWvUsluXpFKKS0xweAqmmxZl1WdhbwBw0bUvCLmFNKSBEmZOTG8mKOCswZhXwwXBgoPiHW1s4OKT3OI6ARwzbHR7uYWmJfIWFxTkslgV1swznztIhL08S7DJr7K+zCtKqrLLKKqusssqeenvsETNMLOPBbb39LgacgJjmGgecLR4ENnZ26MRy5d2rmISUtJVkA2SctFyXEoOyHk93Dx5LVgshofW0mnZZbZrSkUuvUJkTMjRMNCAv1FSyulBGK4D0cmY/VjqWxBc+R/L0+7u7+PBD0jZ44dJzeOESsXqGwyEebhPLaXd3FyELAA57PSNtfvfubSPiNp6MkPBJadDdN4lXUApSldEMyyRq2bYNl0/7vuea5MU0jU15AwBGX2YwGE6DHzO4rPHBNixmIaRFDmVgrAQp6z1k4y5USJ61SiMo/t2eUgj5cyY0CnOKkUBMnwdpDptP1J4l4fMJJnAl/Dr97mF3gpzrsch6YN41pDbRM9eRJgKiC4Vixkid1hq2PYU9S3jwKIzl+775rLWaQqNaweKT++LiaRSiFJQTeOkFKs0w+JW/iXBEz3781AnU2xRhKAoFd5eiB41Oy+io/M1f+iX81t8l4bA4jo+c/IRJlAVgonezWKfTQMzTYGl+A31muW2NIjRjiopshSEOXerve2oXdzm69nD3AHeG9PzKcdDlqM4gjvGorFodZfj0RWLa3T8YIOQT4/7BEF0mJQR+DT4Lww32E4ikhPmahiWSpamJtgoxfS+z2OapBVg53T/cHoD1FJEHDiyuA+BaEidXTgIAnj17EXduEyEgjULIcp5ZBXos0//dN7+HfT49ikIYKNOybFgFQxFJDk8SRCEtG0w0xWgcwWVROZ1JlHn/nhMYBmGeZkbafhbb399Ff59OocdWFtDgEiRJWENnlQQDJ/0E/Xss8Ok6eIZ1zvxODQEz+3rRCIqrpRdKI2QGli5ykzA6nKS4foOiQ8tLq9g8RQm7H75/A2/94HsAgFdf+zSikO7Z74c4OKTIhecl8B3WXWsfw+LS6kztO73WQVFwf0sbPU6WHw8PcXqD4LVBrBCx8Kjv2JAcqRiOQ3Q4enNisWmEBJUqEKX0bvd7ESbMQvLqPsCaaFdubuHt6xQJWWi3MOJI3p0dFx1mePn2lPNybGEOVzjK9d7N+3h5dW2m9lG7LIzGnDCulVm/gCn0o7TGIacyPHqwhdvv03u4sbKOzXlaP461NGynLDWjDVllT3u4d5fa8mDrIQYcCYmDpkE1XNdDnevpBZ6DiFGBWIOKwwFAGqEwxI8CKffJLGbb0qR6ZEci5cA0kmNZFix3CtFPUwkUcp5EcZJMEYswpL0LwO7hEDt71D+9Xs+UhtrZ2UHC69mxzTPYWKVxV/OkESk+e3oTQy6Dc+/elkkBCcMQB0+AJh+7Gj3cSyC4No9S5T8AVwostEt6rcDJjXm+po86Z5TPt9oIyrurAiKb4nsJC7ppTDckCxICjEnKo50Kg09qFChZbbY1hYTkj4TcPgnFEAAunCdY6nf+/r+PCVMVsyw3mOR4PMb9+4SdfvD++/jeGz8AALz97nuGlebaDlotGshZmgNlRrw++mjT/9AaHxP6C7m2zGAwMEXbCqUNPOcGDUT8Yr/2R3+C554jZ2xh4ckF4bI8gVvnMHcao+CcnDSLoUbkuKp4ZFgRoVZIOPyZ5JmBKDQKgwkLoU2zlBZgFiXSvMCE39coBWqcp2IFEmDm2vKcj33uHlcJJHx/X+awOFxa4OMh4sfZj5uMAIXrPXcqtmVyeARMYT+tFNySRSUtWGV5M0tC8GBrbawhYOhBWBZidhhd6WBtmWDG/+g//AfocQ7PyePHj9Tnco5AbClKdU3LEtPnmcEGwwkSDlWrVEIzpJzFIfY5pJ42m8gOaBx94/YPYHHTs0Rhq88MP1hIOdQ+rHvY45C3WJzD6ibT6lsexvtl7pg0bB1kOYRRgnDh8WLn1ALkE/YGpPjY+yiK2XN4fCuAw1Rrq5ZBMfMvlZah8/vSxbl1Kpo4V2vjjQM6iNha4CRTqufnOnj7MgvM3bwNK2MnR9twmREkJhayiF522E3hS948/AA2j9k8zWFl3D+5hTwuWXg+FufKuZ6gy5DZLBZNukg4h07l7SmFuPAwZojwwzeuwWHoW2cxFto09vZHfbz//nW6ke2CU+KQF6mBrNMkNpDGfncIu04w5draBna3CR54cPsObB7o9QDwWFHeFoUp4lhr2jh1ngT7ao0FpMlsc3Gz5QEB7Qe3tx9iyDDNK586i09foPcTuDYifvhJmJrct9X5Gmqcj7HY8hHwwc+2LMw36fcX52q4x0zO+zs9s/EN+j2cXKW18KWLZ7HMzlIvStEd07pT9x34PJhPry/j4kn63STLEcWzj9NavYEB/SmUBjpz1MfukbmulMKQnVBkudknbl6/gUd8INxuSDTb5GjDEkjZUZnkBXoDamNSFNCcY5VZFhYXCcqZm5sqCkfjAcYllRsOsrwcOwVUuZ7qDMkncHieuXDewE8CR+Eqy6QPSCmnaQJHDpeFVkfYxemReo25cXg2BmNM2JGjWpm8hqWJgWcXl5bRZHh2PB6Y+ovtdgNJOX7GsQkERFFolNH/Oqsgrcoqq6yyyiqr7Km3x0Z4HBEZb8uyJdUaAqBQwDclcrRJbnIcZ1odW2Wm6rAlhSnhblkS+khiaglFWVLAtbmUA6anX8uyjH6AUurIqXjqUR5NSFVKQX6iTEkY1kqr2US7xdn3AHAEcnj24rMAgC9+6Uv4+bskavStP/8W/viP/wQA8I2vf93UCVGqgFUyQECnAPoPYe6pKPUd3IASwaNriyOaQhyhqNebSFnf5Pbde0Z/ZJYIjywyaMmQVjyAZF2PeHgAr/TELYEBx+zTLDERiqIoTJIa9fk0ObyMDx95/PIyumdByb8A0JA2mpyEuOBkqHvklfdiGxmHPHWRGUZe+ROzmJRyWlriSI0x13HN93TNNMk95giJa7twSqaBAlyOrrliek9pSQNLSiVRY0VNS9oQrNvzqWcvmFPQUREugnQYxpSu6TPbtj8WjXqSHfSnEUeR5sjDUu+jj0WG2PpBht4ehYYnoUQU02lKQqLkRDTX5uCuUHLsARwc9KkftveHePs2hdH9VhMdnvfj0QhMukKt7sDlBGnl2ChTIEfdMcZlGRAtjkCH2pwSZ7HJfQ03YD2nom7YB4P+GAlD2W7goSZoHA0OD5EXFMJeW1rBM6dojobjGO+9QbWFssMYlp4KRZb1yJIwAqN2cESAOhMRPMeGw23MdFpq2UGlBUKG7YQW8DhSFI5Cw0adxWxhQTFsl8QurIL1fKRrEk+FGBsmV2dpFXMd0rKK793GiCPByWRk6gcmSYgljjhJy4ITcB00rXBwSNGnySTEjWukhbbQdvG5z1Gl7WOb81DcJxM9go4oKtFc2ECzRZEaDW2iBk+yIouwxEwot1nDxQVaT9udOpo1mjcFLHAhdKg2cGqVnj3NCmSmsrZCyu88zQVcniuObeEUi0ouBQ2MJ8z2PHsMdY4Izbfq0Ha5zhbI0rJyd2G0wOwgw5deOEntkwIa0/p4T7K5+QXsb9P1J45tYnODoEhLCuxy6YfhcIhz56jEzr1btw1Zpe55cHk/644n2B/TfFWWhMUipqlrI2GtKRH4cFkEabU9j2UuWdM77JpE3yQaQTEU32jNoduje2o7hyjFSosC8WQycxuPnTzxsf9WJdy2t4cJ32d+fh4JR1TCKEKNWcS245qyJ26QG1hbSolGm6Nwi8uo1Vi8OFem37IswySk+yuljBZQvWFDWpS0bFkWWk26z9raxsxtAp7g8Cx17I9tZFpw7RbpmtpS+ogAYKGUqb8hpYQlSmEhOdVROgqFKJg8HEsICKbNaa1RXqXVVPRICDmlGQOmporQ2jhO9ielaQFT5+TIn9ni48h8me3u2AEuXaSQ+vHNdTx7ngb1H/yzf4H9b3NxvtW1j93MFJgETVgAcFwXHtcNcl33iNPomqKSruubTPx6vYb5BVqAvvRTX8Tq8ixl7siyeIyMQ/N6uIf6hMLWKsnQ5wkxTjPD2Cp0gaIs0Kem1EbKfZk6OeKICOTUhHl3AgIFw1u9XCNmjGV3f4I5hzYqu9kx/ePUAxTDqViXnlF4sNWeg2KoKEkjaIarbEhSRQQ57Dkv2nGawOKhb0vbwF5Sio+NL2momNI4p7ZtG8eJfNbyszL5OY5rASXLUGrj2LhHWFpSfjKWFpSDBsNq3eEexqyy6rk+QqZmN9oF2I9DEhewub81cpSlkLIixh1WdL23KxAxrNPtTdDlzaM53zLv2fMlGk1ayJrNBvqHvMjGU3G0LFVH8naOqN9+AjgLADqoIZ+UjqJEfYHa2643EbJkghs3TfHb/f594xggAiY9WigHh3fx6AHl07VcF9GYD0MZ4HDBQkc4yBlacJ0aWgEtoNItkLEwoO8GKDTLFMBGKTkptULOTvqo10P+SWjpdh0Oj5PJ0CrJbajN+ehwAVDLtTE/R/P7xNlnoVmewTt2Ag/vk5Pz4dV30RvSxtYfDIxDcvzYCYSML69tHsfx4wTX53GCkK9fWgpQZwbtaDhBwsrVo0EPKyu0xpw5cxr1FjkWShfo93sztW/wACiusaMtNSIuity3UqNKLqSF6dSeCnAKMU1TyFWBtFRmL3JMSZ3T9V0cyRvRWmLMB7ORHhjxOhypAyXEdB5rWLAFyzwoiUEw+1xsNJu49DxJtJw+cwYBFxMe9vvGCUnTFC4f6o6fOI5711nU07YMu86rNaBKcT/PNWPc1wINliJwG020uaB1LhUesWNwsLtn0i+EzuCw2vPS3AJsTulQ6RgxO8jDwRiONXshX6KWl+u+Rp+hzmsffWiCC3uHB0iZbZfl+cf2sDSbqvmXFgTBNHWj18dZVqOfTCa494DGdRzFZh1N08Q4UY7jIGUhxyLPMejTmPW8GlocpNBaf+yA++OsgrQqq6yyyiqrrLKn3sSsiaGVVVZZZZVVVlll/3+1KsJTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99VY5PJVVVllllVVW2VNvlcNTWWWVVVZZZZU99WY/7n/+3v/6DQ0pAABSCAhN3wsA0NPrNBR9LwUsIfgiAa3pe8eWcG36KWFJQNMfa6Wh+HOhgaIo6HutAcXXQANa4Ed+ElpL899aaWhFvwWlUOQpAODf/e1fFE/qgIe7kS5/S6HgxgFbD7bwwx+8AQAY9Ht47uKzAID5uQ5cxwUAjEYj5Pxb77z7TfyTf/a/AQDCiYLWFgBgZXEV/9V/+h8AAM6dPYM/+8sfAAD+5//l9zAaTOj6KEIY5wCAJCugRNkUZRotpQXBL0BIgbPnLwAA3vj2t5/Yxv/x3/ll7TnUt74rUHPoT2wH4FeEOFEYjkMAQH+SIFbkCwu4yPmiTGfw+P36lgUhqI2FFpgkCfXVJIbits/XAqx36gCAVuBCiPJ3bXiOQ23UGr0J/e7haAzwezy/eQodl6753O/+3mPb+A9/6wvaqTcAANqyICwaa1II1GoBACCo+bBd/l4K2BY9o21JCH7pQmsUivqpyDNo/mxZNuzABwB49TocHstZkiLhdmdZhjwvuM8klCrHMiD4t9ygDiHpM4REkmUAgF/9nf/uie9QlC//ydfNctm/ctP68Y+ntX7ig/3Cr/+KbgQ1AEDdtbG8PAcAUEKg01wGALSDNvwavYtMKYicxovv+ZAu9W2e5zA/phTinObWzqNbUGkPAJAmQ7PGFBrmvedFYd5joXKk/I6yNEaS0l3g9qEAACAASURBVFwfj0Ps7Y8AAGGao+Axe+OjR09s45VHb+gspctc0cJ4PAYA3Ht4BbZFv1tzXUiLftf1LFg2zUXHcSAlfbZkHY2A+sS3LAz7+wAALTpIM2rX3v4tJJM+tSV3ERU0n2rtFtoLNC+1nSBOaR1SaYKc2+s4DgSv/ZlKUSj6/jc+9+89to2/+vd+SzcCj+5dZMhyvkduoVxcW20PeUHzJk4UVEHvzXFt+B7NrWigzDvRuoDWOT+XwMoKtbvf7yOKIv7ehuPQfSzbNmu07WhYdmHaZPO6c7B3gPs3H/Bz5nAterY/+tM/f+I7jJKRHkXUH0o4iGJ6h9c+fA9vvf09AMDq6iZ+5su/CAD4yz//M7z9w78AANT8eVx66RUAwEuvvI46r1ujYR/DQZeex/Kwcfw0AEBIiUF3FwDwxne/ia0H16gtUqFWbwIAeoMRAofGxdz8Eh48pOuVVMhSes4oDLG0SP32X/zH/+0si4TOed6UY+5xJoQwa89kPEZ/QONOAbB5vWw2W2aPD8dj1Gs0BqXrYDimMXj3zi1EEX2u1xpYnlsCAMzPzSFo0Fruui7CkPaM/+P//BouvvAi3cfSsHl9PX/6zI9t42MdHq212XA1YDYGrbX5DGhATD+Xfgd5R9M/Ngui1sbhedzvKt5kNTRKD0BD/Mh19G+llNkohaYnndmKDBlvWsIWsGzqMKlyLHZoQC11GtjfvgcAeHj7QyyvrgIAXMeB61AX6iyGKHhDFzWkWUyfLYk230cLjdt37gIAPMdDbEWmIbqg57eEgCgdSGjTFCGKaXcWGrdvfDRzE6WQ0KCBprQ2DowAoPimUZphElM/JEkCJahdngXYgga8b1vwecQIXRiHU2oL7EPBkUA5BBzXguXygqczZClNIFvZyLTLfSIQZbSRJGmK1bkWAGCpXcftj2ZsY5qgYH9ACQsavPC5Lka8IY7CyGwcQlpwXPr9wA/gsSNkW9NRDWGZ+GeW54iHtKiFkxiORwu647nQ3DeQFixecLUGwGOhyHPkScp9nMNx6W+VAoonzIP/p1YuQE9yRv6/8lt3b97Bq6+9DgBYXlpBp0nzplGvo+HTYldzLXgW9aElbVjc/YWQZoMWR9YYaVlmwW56PobxEABwsHcdSvOGWuTIM3Zc8xwZOzxZlkLLcgIqsyYFvoPlJdqoBuMYo0k8cxsH4QSTMY2Hhi9g85hs1AIM+wcAgHicQ/LCHdQVhKTFXcrpWlsPFiA0OYep1uj16G9rvoeavwgAWJ07haF7SPdME3Qs6k/PmYMrqQ+70T66wz0AgKsy+LbHTyrheNznQRuj8cFM7cvDHGm5dngeYl5bC1Wg2SBHVWqNaExrX6YFJPexUhayjPrD9SUsfrmua8OyyFFxpcR8i+6/uriMQpHj6XuWud5xXNi8jguhINiR1EqbseALgZa1Qg+tBVzPmal9APBw5yZu3t6ittgBFhepvx9u3UOeUbsO9h/ge9//BgBgd+cOwoj6bzwe4M5d6uON48ewyE7I+++/Da1oXCxtnIHFjt941MX29g0AQK93G55H4zeOIoz3ybEZDifYXCPHII2B8Wib+lMIs8YMRwPYdrkyP9m01mYuK6V+5P/Rv4UUsPgwlxcFUj4QpEWBcUhzYhhNUDpO9XrfOCqHBwcoN7cwSbC9/QgAsLWzg50uOX7rxzbwU7weXPI9DCYDAMDC/AKShO7/7uX38M0f/BAAYNnAb/7qrwIAzp8+82PbVUFalVVWWWWVVVbZU2+PjfCUcR0y8fFwuTnIiRL1+pH/Mb1eQaOMB0hMPUf9I9eb74+eEo8EhD52eJRqGv2Q0+gTFMyJYRaLJiMM2KPsLC6ZSFGr5qDGMJDnunj+2dcAAIPRCNvbO/QIUpqo0/lzL+IXvkJe5/5OF+++9y4A4OTxTXz3u39F959r42d/9ucAAHu7W/jB9yj8qfJiCgUCU+jwCHaooSjqAEAXAjqb/SQtpTSQH6GF9FtpliOJKeQ5CTMkWWF+2OEQb2AXqPHpRzoufJtD/EWGgv3lNJNQOX3WgQsly+iJBVj0nJlWSDWHgZVGzs+fKYVJTP0mLQn+Wdy8cR3DcDxT+3RWQOto2l5+J3niIOMTq3Y8Ay1lWiCM6Z27rov5Dp3Wm3Ufvk9tdWzLjHxFmAfdJwfyjD7HaQqHQ+RCCBOuzZVGwaeaJEkMRGLZrul7pWHgkn9V9rH5KT4OAf/1f8T//tGLxZEP/y9EiAAgTzIE3J8LrRYWm20AQL1WQ81nSNH1IHgs21mKWnsBAJBJhYRPlcU4gs9wqLA9FHxyng/qaPkUFWl5dewxhBBGB1CaTsvWkfVGKQ2XpwQsC8rh6JyWKMDwkJLIi9nPjbsHu9h+SL/r2VtYWaCoi8hCLC1QpEDLBkajhP8igy7o+fMiMhHFw2Qfhz068VpKY9QjSKvu9nDm5MsAgLWVk6hP6J57+zvIE1ruu9sTBDW6/ygdI4ro1B0VGYJlep5mo4W5DkVAWq157B9uzdQ+qTRihqgtuw4p6Xe0SmBraoen6pivMXQ5X0etQc/l+zbqDGn6LmDbHLGTJrMCOtUYdGldWFtfhuVy1FZlYNQLUgoD/9u2B63pGlVMIxXuyhLSZrmB2MiK2aMf33vjz3Dz1n0AwHAY4vix02XrkSX0bMPBDq5/eIW+1Tl8jx/OlTjoEZR27drbWFygCM/l997A0kIHAHDmmbO4dpn2hls3rkJxdGixJdAdUrsO9keA5si0BISa7q82R7GGk8RE8/OsQBSlM7dRCAHbKsP5mKILWkNyVHJ7fxfvvncZAHB42MVgRNEnVSiMB/R5lEYGdhQqg86oH/L/i733epYsSe/Dfpl5XJ3ydf1tP+3G7JjdnV1gCRAEFlgRIEWApAgGRYFPUigoPUsP+hf0oOCjQlKE9MCQGJBIKAKE58LszjqM291xPe27rzflzfGZevi+k1U9O9NTDSCkiNXNh5nqe+tWnZMnzZffz3xFAV5GkecFJF9/X0j0eC8ZHY3RvH0PANCoBFht03qQFhlyTZ8zGPTx8b2HAIDLV69gg9GXz2pPDXikkJA8wRYxOnyCJ2DpBWYhfBELbxOATSYZ+wPAwHJ4CD1bhK7E/LPL9eQTC6+9HCnm3yUNdIGl2zs/+HOMh4Trn7v8PBxe4e7f/iGQ0Yc2GyuYdohP8KMffYAHDwne8n0fihfoTquBf/7P/gUA4M03v4uPP34fADDsdXHv49v0Oasr+Pu/8VsAgP/2v/nv8K/+h/8eAPCtb7+BPkMmjusCggKGaBbZdKIBDWYAEEZbCGyZVhQFBENaeQ4YzdwbnSFJaNONU4OCN2PlOHBLaM91McrodTTNsL1Bae6VZmA3/iiWkAwjeV6Bokw/ewpCltyIHEztQY7cpjmjNEfCrwUcTKc0OVbCBpqV+lL356j52My1RsYwltYJNDjwMBMkkob7oFCQjCtjlqE/pgV6tV1Du0kLru+58HjCK6GhJPW973pweOMzUkCX/C+dIWG8PNewAU+W5ch4ksscULzuFdpgOpsHaX9T7UmmzCLp7lN+jgWY+jPPCGYegS808fmUnGdu/+yf/nO7qLUcF1VdQsQCPvdzAAl+FCj0nCvlmPm9FEbBJNS3Qa2GWcoBZxrbTbS+soEgpEB3MG7g8JBhKX26gMT7lovnigIaKX9+Co8hy9TN4Tn50veY6QStFm1ys8mxhXwub19HKyBYone6h7igACaLMxSga8vlBGMO6k4HEXiIoebVYBK6aLftwHEoOIxmOZDTWK3564iYN9NsedDMyVkPNrG1SpuEcYFWp5xzGRQHdch8BGJtqft7/oUWMp5ztWYVntMBAISui+Ndgi0efPQQyqF1ZHXtHG5eof5wPUBwcFJxgoVtJrfwo9Qecj4HtatraHXo2hUMQo+uXRcaGcPkaZYjZqg+jmNMGVJB6kEWPEd1gbxYPqh//Pg+ipyfSTbC/h5B70oFAN+7RAadT/nnBttbdJ1RKnHSJxju8OA+Bj3qk8P9ByhSCt7fe+c7ePiY9phe9xDNkDri/LkNDIb0veNJDs3XH7geDg+IM9NsCqQp3ct0nCIqD1XCQDa8pe9xMpkgzvmAiifhrYjn1h9/+/t45+13AABpHFsqAxyFggOtwmjk5V7l+BCKxqZ2JBIbUCkoPgwf7u8jWN0CACRhC30O6h4dHuGkTweF/dMuBB8CLl2+iHqT9ubVzXXs7FNgfvNKGYQ+2c4grbN21s7aWTtrZ+2s/dS3p2Z4hJgTyn5S/CHse4Q9SZoFOMrYvymzRJ/8W8rllCl+jfnJ8wk91kKqfn6Sl2IhCwRKewIg6OYZDp9vvfHHWN+4BADwAh+371DEery3i//kH1PG5sWXX4MfEKP8nXc/QP/gkG/XIOdrfaAzvHjzIgBgdW0FzVVKTypPYRRR6nm0H+O733sDAPD6V76Mr//K1wEA7777DjKOxKXrozxsxLPZXEaFeQbGQKAolj9VFtA2JRxnuc3IGW2gyzSwMPBVSfQTKB9ZZCoIVum+du8/wOFtuvevvbyJBqc2C1cBFc7qGI3EktrmKrys0IBgFZMS0AwDVMMGAlakGKR48RqRzVqVDvYHg+XuL9Pz7J8UFtLShUFanvS0Qcbw2izNcP4y3ZMfhtg/Ijjj8ckQU84G1EMfFb6/wBVwyv6QiSUtO55vMyNFoZExpJXlxip3isIg5YxTnswgFV2PERJRUsIWn9+EEJ9JCP7rKLPMcsDXT/7dInq28BGL1/KsBOZGtYoKE15D6SL06TTo1esoAUbXc2CY/K4B5GM67ksIq7ryHCAqM2lZjnGXTtE1vw7pUVZHaaBTq/P7XcQRnSqP4jFkqfLTBrKEVQoBxePXFUAhWIEqDLzPIQYstu6wj/UOZTSMqCFKiVR8dHqKB709AMB0so84prE/HidQDM+pCnDap6xBoRU6DVpj6sE6trZpPF+9+BLCgLJkx0c9S352PQ9CUl/dvvtDnJxQVntr8yI6fD2ZzlDx6W83z1+0EK2CgxZnZD6vvfL8NgxnvIwADJ+pPeUhGdF39vrHSFLqtJ3DQ3QPiTT9la/cQKdN3yP0PBuRFymygtVYXg2ChSK5yNCfULak6lbhKZ5neY6Us6rT2RjjiN4znk2Rch7ChAaSr9NTDqCXz5jvPOwiTug5tJp1eF65F2Y44rWk02lje4vVZL0jeCxoGE0ymxmfTLuIZyywkBoZk34/eP9dCKfMVhaYcPLx4HiA3pDhxxRWRVqr+kgnDPGMZpgwpJhkOXyeQ0Lm8FjFuEz7/T/9E6ScQjQLe3CWpXh0SnDVsa5B3vgiACCU8/3YCFgRiTLCigBkUIPw+blKD4KpD0IJTE8J5ru/dwrJNIRUeTjiLO8MCtGAvvfw6BCCN67VzU1sbtLcjfMM9+4RBPYrP/cLn3pfT5+qCzyAcqtd/Bee+NmTix1BYIu/W/jYhZ/P9ykxV1lgLl1/8rvmKhohJBaQtIWFW+BTorPPbC/dfA6G1QjvvvstjHqEl7724ut45Qskd9u8fMkOUqMLPPyIpIHI5xLy5loHb3/3e3xtKb7KsvHJZIYZT/RkNsWf/fa/AQA8fu+H2Dx/HgCw2m6hyYqUPAHSUnGWFZiwRC8uYoA3g8uXb2A8HC59j1oKCMZ1s8RYxZbUlmID3wE8sHIKEoVHv7h582fhty8AAI6Oh+gybPPe/S5ef45SiXmubZ8rJeGX6c9Cg9cdGCOtWkI4QMUl6Ojl51/HlNO6B8d3sL1Nn3nvzg7e/ojUCX/vc+5vOE6skkw5yo4jGAFRPqBCw+GFUkKD13JsbG1BMG/g9p37eHhCm0LFT+ByEB24CnWWpVd9BVcy5whzybkWEjPeiKMkh8+QYDVwUBTUZ6kuICTDd2ou+12m/fWUVs/+t+UUWuprFzD+Jz/js4O0T2thYRBytO8oWOsAKYTldmRpCpejLc8LLIdKCyBlWwUvCBBx4Preu9/EH/3e/wUA+IWv/3189Wu/xp8pYTgYCJWHRkhwwqlsIctpDTBILbdAGg3J80aYHGCIxVESlWB5hc/D3V1EaQnV5ShYNXbn412MTuj6axUDcEA1mxbwQx7bEXDap7G01trA1god1K6eewkbbQ7gvTpynnRFoVFCLJVKDSenNLZ393YsFPho5yM8fkzzLI40bt+jte2Vn30Nq+sEY22srCCsluqtp7eK40JLXr+UgrYH0QIzVrNJ4cJzaT7NEoMfvUObVKtexa/9vZ8HAPRGXasAUkpaZdtk0sWMlVx7xzMIRX1W9xvosQKy0Nr+bZamKPhZCV9jPGK5tDAIQ5au+0CaLn/4SFMgT+kaomiGJKJ+DcPQjlkBiRFDMIEjcXJM6/X+4QSyVPbqFAk/hyB0kbLCS4sCjuAgVzkWKhpFKXLeG2ZxbL9rFidW+m2URlDnQML34XHAE0VjBEs+QwA4PT6xCl4DWOrG/cePMWucAwC0r99Axso4SkCU9BRt92OjgcyuAY5dT3IjLIVFiwKHO/t8/Q5mET0LGaSISll9IXDpPO1DMpvi6DEFSHlRQPPeRurxp7czSOusnbWzdtbO2lk7az/17akZHr3otvMTJEUm0P4EgXlR9iEWfvZpchAzN9OjNwLgk/MCsdkCXdrMMzlGzwnPWCA/L+fPZtvqegN/8u1vAQDe/fHHaNWIZKecwBJrYTRyTgkkWY6MT6EmLWzaz6tleOOb3wYAtDyBn3uVjApfvHAB/S7BYaHvI2JS7rt/8R38BWcEeuMTFHwqC1ADOHKv+hXrC+SkLlyO+s+328iazaXvURfapgAXbZOkBHyHPjNwJAJQpsvkOUImj772hZcwFnS6/uLPfA1/+t3vAwB+fPsdnGdTwTCYp5+FdqyaTOcaRpeZn/mJ3RUuWg1K99Z9hdRQyrlTaeLuXSLrvX/nHiK93Ml5EhuoUpWhCjh8wpSQVkmmNTDjDECUGHR7lHVbGU+soVyqBUpxTO76CCvUH1Geo9ujXzgythCGzjXSvBwXmR2btcDDZpvVQMKBy3iY0tL6YuRZZgnPf2NtQdUHPJnotN4ZS4iunszGPvn++e/MXD2JT6wDf8XbqlYC+Ews9x0Xktcc3/NhuA8d48LnU2UhjM1cSkfB5TmaFgVODmkc/fAv/xhFTuPr4b1b+BpneLSAVUZWPc8anBWpwd7xXQDAZHwIk1EGBlmMgrMAyhHwGNaEdGCWMGYr22m3jyimNaARVsAoDPYPjxH1eRzWfVTZMHNz+zy8Or3ppL+PVot8qi5sXcFW+zJ9TmUVnqL3O9KgYLlSxVNIUso+9LoDjEd0L3s7PfR6lHEIqwFWWB1UC6tIZvTzN9/+njVyvHJhG9UqzfXf/MY/fOr96UzDYXg7ihMUTPQ2RuHkkOacMAqmhMuEQMEmp9/9zo/QWaUM76uvX4U2fNKXHlTM2Zt4AsMqniItUK2zWCJY8PyKMyhOXTu+hMNE6HgyguTvdTyFnL3SppOBXd+XacYkcyWwyoCMjWgHEQpee04Oj8EJJKytr+DDj4kKYOAAhtah6VRYGLwShHYvUa4Dw75QFde368R4MoXi+eHAWPVsWA2sosoPPTL3BTAaRjZD4oUeouwZIHSDOeIiBMYsqummAs1NyrRMsgKa4Xqo4hPzvtzLFXL+nBzaZmelEBYWT6Mpuof3AQBax8hTuq+KENC8R3748AF2ewRHn99YtdmnUGgUvB8XxtjM5We1pxsPalg+SSGM5UZI8SSv5hPaKfqvmINM2sByUYQQeMI5eSFAWkSxFhfTRbn6XNIuoEugTRsUVi1jYJ5hI/n2976Ft98kaV1vUqDPi87u4SF0Kas3sB1ZFAY5p8KFY1CSXXKhkE9ocPUGkZUttupVNEqTu4pr2eWT8Rh3WbWgfQ2d0IONsxgJJzELR6IosVwNOKysePDBB9Bq+UW2SDMYhlIcKa0BnwOBCqdXG54PTzB+HkdweRF69PGPcfnFV+ia+0e4c5c2g4PTGMdDupeLfmBdUR3j2NA2S3QZ+yDwhXVR7TTauP78y3RtyQSzMUEImxs38fb7HwAAUkjU2xtL3V9WCKQMN6iCVFt0IwU0L3aFMTbmlsZgPKZN5/HuITSng5VfQcgbR7UaolajoMV1HAufTqMIfU6Lx2mE0KONYGurg2aVU8nJCJtter3SCIFSFmuMVSzM4mSuXliyfZq535NS9Pnp4JOo7uehvE/7/TLw1iKk/FdlFNVWOnB4gZNazOGqJAXS0jIBMAxdTJPY8m2cQsKp0/OKjk6Rgp5vkoxRb9OG7rgFdDk2ivnimwvA589Za7UhPTqsPLw3w3hGm7RSEkGFnrUWBortGYQzh7WXaSbLMWQOhKkXqDgMIxdAs02fv7bSQKdDi/u5C+uIeOOHo7FevwIAuLH1Gpoe3ZfruBAcWCA36B0TJ+bunftIE4a+ZYz+uFSiaVTYDdlRDnR5KHEVqtSFOBie4mhA997tPrSr/OcFPI50EfCJYDqN4DD/aDzM0D2ieZMliVVdCTg2eBhNUvzBHxLHUSmDc+cpuPMDCZ+Vn0Gtg5Rl6TXHwyo/8xwFROnsryQ8HjuVsIIBcwGTeIaNFQqoGq0m9vcIFlHaQbikIhQAFAr0+DMzo7DFfM39vQOMWfG5vV7D5jod6tJcYswH3bWVFho1Nq1MYtRYYXR62rMH7LBatdLyiuthyOvNeDRD4ND9nltbRcGBU8XPUW+T/YA2wHRa8toAVY53OTdyXKZpY1DwYQ5KoFtyG6++gLBDhwOpFUpyqFba0k0LI60NjRHCBicuAL9UXkphx0B0OoZha5L06AjOJXoWhQQKVoSNVYHhMY3xw94AIdsRXAg91Bj+FVpDfY569AzSOmtn7aydtbN21s7aT317eoZn4bVYMNahIKokHC1GTeJTT4oGAnn5Lr2QyVlI5RiIJw0GbcZm/l3GzN+jF+pwaW1s3a5C62eCCk5OIqy0uVREWOD0hE5HR/u7yNOy9EMKYUpTuQKmQqeyXGFed0cYm8VKATw+IcLa5e4AIRMH8zywYoA0jWEsoU9CleUQpIFfvkcXpEAC+xqV5QriDHiGaN2R0qY8lRSWEOlKYQ3aKp4Pwb4bTpage0Q+IN/64C7OfUieQt/+4CG6XbqvcVpgxqaFCrCnRPL7KT8fcPnzg8BBp0mvL261UAHBDIPJEBcvkjLr8fEAMz7lNJsr+Ojx3lL3t7F9ATO2GpdFioQhgzTPkC9kOHNRehoJRPyL4/4EKqDTbpSmlvCpswSGYcBWu4VyDBZGo9mmU1lzpWNJ381WHR57c8STCJvXiJD+2msvwJQeQQtEvcLAprOXaZ8UBHwaGXhZfvD/m+W2nklBNo7hcw0k3/dsyltoA8kwie95FsZSRYoyn+hWPYwHXBNKAcYpjQcVDPtI9U72MOiSima1vg6/rGvme1AlLCUlwONhOHkOGc+bJDpBSR/N8xwOl3vIi8KuDcu09U4D4xHD4I4Pj7MhnZU62g3KLl4410aNMxdhqCBZybN55RVcXKPMqJt5xJ4FkMURULBySSo8fkzwwP0Hd1FluLnWcDEY0JzThUanQVkGx1Fzgz8nhWYxlis1/LhcdzX8YLnn6CkPVS4DItoKLvflD998A0P2n1lfb+FVhvzf++gO7twlwqrjOugzsfqbv/s9/OLXvwAAOH+hDc+lz1Q6wPSEnvPg4Bi9PbquxmoTYZMIummSWMhxMppYiK9Rq2E2pmvIsxzwy2xuA84zjNOttRa6XVLXRdMUx6DXsyhGwCrAtU4HEWdaHh7GNusZzYa4coWydHnfQcBGmFl2bEvvbG21oVnocHx6arNrKytt9E7pu4JKG36FaAfdwQApaF0WQlnoPvAq8Hn9TfLYZvWWaUZrLtMEzIRAyt5RRWMVYy6VY/TCzj8XSSIXAqmZ00HAe1sOY9WWwhhoXi8Pj/YwLDO4sylcznrKPIcpeE31XVuH0KQZRjzex5MpLjN02Ah8hP7T7/FzVVoLVBqbtv4Ea2cuUV0YNIvbsRCAFKxOEXIhsDFPpMItsxvGBgOa3kg/Fwu8AbEQIMm5ezCMsjDGMi1KAmxvU4ouCAKkV7hwma/wzg+IkzOZ9iEZuni483Bet0kJUigBmM6mMDEPNBQwmqV1+YJMXksUPKiLXEOWyvLCwOF7UZi7imIRwhOAskQM2PTtMs13Pcsj0UVuVVpKKQv/CFAKEQBcBWvQdf+oi/t9GnSPhykmXAMrLlLrdukKAWFVBRblgwo8VJiL4AcG66tsglY16B+SWdf6+g08OqQJ9M7HH6AaUhpbCRfT0XKy9LW1FlorpLKpB8CoR4va8WiCox7dx2g8QcaYPaRBrUGLRdjuIOH7aPgCeUr9OugNkDHmvbG5AYdhydNuz6q9HM+1gfbpwRF0nxYjOR3ibZbLnh71ELKTs3Id65QrHReCX7/6q0vd5hPt/6sioUu1T6PrLdF0kVl4UTgKLktYHeUg42ckC0kqTgCO79nX/dEUMTsGT/qHuP3BXwAAQs9HyApIk02hU3ourtpCxpyciu9D8WIa+L5d6M+tn4Pixff4MEYyY/VWniDmv9VZZvljy7SNlRbaDZ5z0rV2B6JZQb1a8lEMYBVhCpst4kxst5+Hiel6poMeiozWqiiK0GpREF4YgcMjMl9rNEI4olS6uRBsaCohsb1BEIiSAjEf7OodF0mN+rBadVBohvDyHK1OuNT9yRRYDemzZ35mN6lQedjYIH7ktZvX8ZWvUgFN33Nx7x5dryjm0JWjJXZuEe8FwwQ1xto+vH+EO4/o537FQ7NK68vGegOvf5WCwZVWE47Pthe1EBMOPLQxqHGQ8PDRI0w4YFw9t4YoX94EdKVdxcYajanRdIruKa1TjuPPTR+jHAUbPfb7MVplgKkSpHnJOQGGQxqPMBKCVbg6N1bKHYQVW1MOKOCF9Jy9H+W+rQAAIABJREFUQCArlcMiQMRSdCEUVGnoJ4GMi1trXVgzxmVaoQuwfyGOjIsRFzl1jAOXHfmNMFZBpgXsegYIO5+IP8WHbVMgN2Vx5hQpw4L9Xs/y4JqBAw2bFUDKyl7peJBlUgM5ZjxHo1mCLf7WWAjEn1OB4AzSOmtn7aydtbN21s7aT3373GrpZv6PJyCtkpooFjISQizq4OeVaSGEPdmKharG0MZmb7R+MsOjxUJWZ/F6xPw95fXk+RzGWiQ2L9NO+jHGM4ocwyBGo1oabAl88y9+AAD40zfeth4rjx4fQJZVvyHgs5+EX/Gxepl8MVabLbRY4VPfXsXt27cAAAeP7mCzRQS3tas3YVbpVHFyeITRgCCkuEgt4UuLMhdDp4Gyd3OJeQp+ieYIIuoCgBKAw0x/byHDUziwfhUjaMgaXf/XXrmKiaCT3v3v3cKUMxcKBqOEx4P04HMlXqmcUmSGwJPwWUnQWanDk0SgjNIcBejUkmYO7nPl4eEwxtYGpXtvPTy2ad3Pa0URoc2Ez0bFwOGs0mwUYqegU/k4L5CXXhVZNs9CKViL9lYzxIs36ZTYPRni8R7Bm41GA0lpZDYe2TRxrdlEElHWKBoOESZ0yvLTAl22ej86+TEaa/TMm80qFEM2rl+BHyxn5vZ57VmViU+2z8oU/eRnfprq6yd//gkrw2e4NKmNrUieC4OydoLjONB8qixkYdeJzGgk7FM16J3ikJUe+3feRBTTWPNaTSjJ9dHg4/CQxATbF16wXi3aaPghwyHDEVw+nbaqIfKCMoeT8RqyKc1Rk6f2vpTj2hIFSzUtMGW1lHICrK5RxqFZ9yAEZQSSNAUKJr2HTWzVCfJ1sgCnpzQm03iICZfESdMUIftETZMMjSadxn2vgh4bDMZJjh5DSkYYC6VJR6O1TlmDYE1hd0bXttaooc1E3v5wjGptSR8eJVD36FoqTmgHx2/82q/iiOGYuw8eweVx96VXXsa3vvMuAODh42O7Nk2jCPcfENQ1GUwg+dR/a+cIEyb7e4GHJKb+c5SHN75N9QtrrRBXb14GADx3o4GIqQAmz9Fp0fvPb51DzJB8kqdIpstnPxynQLNGY8T3qjjcpzWmn6WoV7kf/LqlI+hCYmtzGwAwGB5jPC0FMNIqRKV0bEZof28f7Q5dp1fxUSomdJajiFncose4fecBAKC1fs6aUwrlwuFnG0VTBDwuHK3s3FqqGYMhlx36WCsUgtY5Z9pHmX3UENA5XbMwGVw2DNRwbCpFGAG39CHLChSMBGR6iviEKAs6ndMHlM6QcUYurDWQlHuhMaiWC41yrJoqMhIxjwc5nmJ/+PT6i5/P4SmBOTFXYy2ATRBaWDXTYppdCgFpeR2CjYmoAxYLE9oQSQj7VYXR1uWT+Dks5TXaOmJKSAx5wgvHh+8t1Al5hjo/eWGQliqaTCMaUhDiSGXVA8Zk0KyuqtUb8AOGpbIULqcPv/Daa7h0ncwGB/0ZusfEFXjn8Qlqa2QKdrh7AP4YzKIUms3Urv/szyBi86rJeGil+kmaYsgGg1GUYNEKQH3mRvWTzYWAz5u97wUIKsyBUAKCZZSaqorSa89H0GKVAAwCSQvov/yv/iV+fPshAOD3/v3v4aBL1xzlQE3NA1HJqVlpJBweYoGsIM9owd057mE8osmxd5BgwgqG65eex/4JTaCPd8fYCJdTTjQ3LiMTXEdHAq5LfeMVETZ4gVtrd3DI/KOjXg/g/tB6Lp2///AQBwe0KNerVQuXDPsDhJxSf+Xl6wiYF3HSHWPEa0jsTJGXhnKQ9hl60kHNY66AH8LlIMcNanArfwMBjzB/ZU7OT1pKPPGvn3g/TcNnC66eBXqTFQ+6lLDmOQw7G/uBj5Tr+uTQthDucDrCe9/7IwDAbHKEwSnNuUxkWF0jWAXKQbtFUMqgN8R4SuMrKzQcDj7zvKBTAYg3oHhsyKCKkA83zdYWplMaPzJ9bNWWApnlFC3T8sTgeK+EQByEJfQS+IgSCjY81UBF0JxrOOsQLMlO8gilkaDWGoKDg2a7ZqEjIXKcO8eba78P4ZQy6SGOT075Xnwolz5nfXsV21eIw7hz+tC6W6/XG0gc6qswbFmo6fOaFxTonVKgooIAlRorzzp1tJv0+nh/FyHvPF6lio01ej6Pdw4hmcoQ5Ropr0fRcRea4Zgsy4iHCCBPYvTZrTyPE5T4YJTO8PaP6JB56eYlNNfpey9vr9uaeNWKj4rLh6RmHYNhd6n7A4DBoIvNdVpXTk6GtOgA0FpiyoUyj06BKvel1AZV5uocnxgcHNFz9gMHMauTKpXQQlpZEtkDvOf66HXpuaWjGXxeT69c3kDz+esAABFWMWKzPqN8DDmQM9LAD8q6jLkt4rlMG00nGAm6/jxsIuf1ICmSec0sYyDK4BOqpIZCWzMQYJTOkHAtyQE0KuuUFFjrtDFiaC8ZjYAJ7XPD6cRy01oCiDnoymUGjz9VQcDlgOcgSdA1ZbCkMZ2MnnpfZ5DWWTtrZ+2snbWzdtZ+6tvTjQfFXHUlF8nJT3htiCdIyLbmhln4W2NsZLXgQP2JM2RhfyLkPINBdbhYx68FcgtjZfjmn5Fh4N/++b+FgAmOSS4ALK+akEJahr7nuPD41CeVhBLlqQkw4CqvWtuMU73ewCVm3LfWz2HAZl7dkz52d8hjJ02G6L3PEXqSwHfoVNEfT2A0vf/GjWuYRBTVvvLln8OrL79gv0tzBP2Hf/CHODh4zD83tor6Mq3q+2g26IRWr4XwaqywMzmimLI0eW6QsWV7nhsU3P+pUbj4EtVL+eXf+q+RMinsi6+/jv/9f/xXAICD3gSX2TfEe6JsiEKdSXa+U8POEZ38bt/fw4PHdFo+t7qNqxcp6q80VvG7b/w+AGBsHFxbWy4DcmH7vK2IHXgSSlBfVioeNplsHsoAgll4SZIiKc0AAw8hk5C10Sg4k5CkGWKGq/xKiHqTyNTHx2N74nK8CgrujxzA6gadrG9cuowae21MoykChtgqYWCNwHJtENYbS90ftUUM6Rn+DJ+dZRGLk/GzvuuJ988NSOn107M9z+rI41ZDS94PwtDChaM4guLToMkzzPgZPfjoTbz39n8AANRXOxjHrCqZ5ajX6TTb3DiHnCsx+26OnNV80+kEdYZbhVQwXFEbOrdlD+B4cLnvKl4VjSaN0zzTSA2l413lwbjLEXoBIC8SeKywDH0XPmeHRCEQs4+XVwmwtkbrSiNYRcQCAimBrCTXKgmXfYGiNIPL8Em1WsPJCUFv/eEAijPfuSmgWDiysdZGZ4V+fvXaedTZR2an+wABexwlUYwZm82F9QaqznKQVj86xpTLZRjpQHZde10+f4bvaHicEa6GLoym+epIDSlKNaxGoUtFo4Eps/C6mI87KeGwGi/LYmQx9UGcJTjmPrh17x4qvPZ94+s/i402lQtq1n24kuZlu1EjmHLJdrw/ws3r5BF2cDCC5rVEZjnYKg1hJcBFzgIVhYcWp7QcKWwV8miaoKy944c1BG3KUitVswa18TRF1CPYtlJEeP1lGhevvXQeEY/ZcaxRCalvVVDDwYjG+IePdpBx9iNNUjiquvQ9fv+tNzFZuwwAqH35OpVTAeDouVqb6hZSU5jX1BSAzXqmWY5Dvt8CBYqCxrL2NhDxM12/9THiiNbUAyHhceYyTxKk3KGhV7H167RQMEVJ+wBmKcNt1RDXz5cU5k9vn8PhkXNjIfHEL+Y1rbDgyAgByZwQdwG5ElJazsknF1+zwOEp93Ctja11VBQaealU0rAS397xEXxOB6NSxbQoJccutFg+GGg22pC8AHmua83OlOPYwnt0jQylSYEGF+175ctfRcwLTfeki+4JFwI82reKoK2tDVRZUru7s4cTljMW0IhjmmTf+8G70CzdG89ybG6RKqPRaFh+1MWLF1BwAb1nDXhqtRCdDqdvGzWIgDf+LLJ1b1SSWcPGwmgk/Pk6qOP5r/5t+iDXA0PF+E//xW/B4YXqjX/7r3Ghzf0mDXyWBooih3LpM+8+uIP9HkEO9XoT5y/Qe86tncelbQoUfvjhXVQr9L0NP8fF9eWK3R3tPkBtjdLyynExG5KKQ5kcJyxVPjrqozcjSK3SbiKsU0DiVkP4ZQ2eQiPlIq5xkpLcF8D5ixfx0iu0UHZ7Pbz79tsAgPFoTDXOAKxtbuGLXyblyWtf/KI1uxtPxuj3KLhrtTtweHxleQ7nmSOXT/z/aW99wpDwyZefHqcIlMiM7ygop4RzYdPrWaaRP2H5MH/PHG5dMA0VhiDsJZuQwp5VsiK3a44jBARvDLksMGNYov3Sl7Bx/036riRCd0IbdCI0cubteMrB+x/TQcGXApcuEgweZTk8ftYrqy1E4yl/WQDNBC+jC8uHCAMX1QqNmaS1hVzTd6VZjiJe3sFWOTG2t+ja1lZXUG9U+d41EpZJeyJAs0LcIV+5EOxTkeUp6ixXL4zCvbtknDedTrHOHBHflXZtGI1GaK2SnLjRriM8oPvyHaBao6AuyUcYH1BwkMQTaMlmcFli50JdOUuvN8N8ALco55O06pvjwbFVjAXVwNbVi2cTW4vK6AxFqcRJC2s7AiEXTDcLS3cQSsLwAulIBcFw380b122B39t37yPltbjmVNBmDuUsnUFrmt+BLxDF46XuDwBcp4poSt91fDJDygdFRwGl0W+Wx2g1SoO+DG0+ZD53roUJBwDj2czyQVs1hSor4UbTGfSM4bzxMV68TvDsq1c3sMafU3FG0GyuOYtTBAHBgk4hUc0pqGiYBH2GzIpkhuQZKDzjo1PETdqHQuVAMIdOGmFNWLXQll/0hCrbSBRlbcPJKfxxeTgIEGoKFDUKZGwq+PpggGNJfTK6dMU6dYvpAAVDWrM0guG1M3U8jEvH5qAKlw+1Ghqd2tODujNI66ydtbN21s7aWTtrP/XtqRmeAgKqVFfBWJ29VHJOQpYC6tNOngskZ22ArKy5AYOsDIMXDNS0FtbEL9d6nlUX88R4LgCHU2vxoIdfvUrpvZ7fQMFRbTw5QB4tp+4BgKAyJzsrKWD42nRuUG1QxL21vYXLl4l4PB6PsLtHcNXdu7eRcIbn9PAQ/QGd5CvVANvbFMn++q//R5B8M//z//K/os8k5Ea7hVW26G63V7G6Qe+v1mpWvZPrYgFiiVGU+VLzWVDEp7dKI0TIJ0m3EsAwbuAA8Pn0qPOJPfFkRWahl875LaxeukbvAaxHiTYGv/aP/gkA4OjjjzA7JoVMzfcw5OrwgZtjwMWpdrqHMC6dum5e3EC7T6e6S9vnkZxSv3VW6vjCRTqp1H2BrdpyRMn93bu4sUUn2bWtJlROGZ5bu/t46xapWo7jHJ01gte+9NXXce0aEf6Oj46tUWGeF+gPKR0/nUWQDGNduXIFL7z0EgBgOJowgRzYefgQQ7bfX1tbx/omZZmGwxGmDJ34ng9XsYfP0SmCgE7W8UK142XaItdfYP6PRVjJGDxRgd2eirFY+gHwXJrHgaes3bzrKGw0ady16p49uRmjGVYGesMM9w9p/M7SYmG6m8/IGj2bgqxIc1tTLi0yKD6P5WmCMv1UObeBLSa55o6H3glV+n7vD/9PtHi+dh+cIp6xT06R4KWX6VkXUQQ9o7G29/hd3P+AyMMbWxdx/jKZ3FW8BhKGkOIsRc4Kk3a1Bskp+CgZYcxpd4H8J4rrPK2FoUBzha6/U2+jYHXKUXeIyYg+Z2Ozg0alrGuXWUPTSliBlDR+ev0RcoZhms2GVV1Nx0N4LnvfBD4K9pIKPIkGZ4eUVCh4Pd7f38U4pgxPluVw+HuFMIiYEDxJElSWzEZOivl7q0FoS+BM4shWzTZOhl5Cme5m2MbKCs2zJBW2urrKDVLOaEMKa0bnBAqGlUoocki/zMgbVH2671//uz+L9VWai3/+xvdxdEpwyUqthv4BzdfB8MSKUoILEkmyPKQVZwX6XFZnPMlh2N+oWq+gwRkkIMKQ94Otpguf19bXrm9jzM9k72QAw98r0wgX2wwbbqxgUpq6Ohm+/DJBqRfX6pBM3pYmtcrFySRGzBk4pTxUa9TnrfY2uhG9fuPtj9CbTJe+x1oYwuGyFwIa5SavlYRTEqqhoRj5cCDn1dXlnNriTCJUSnGRG6Bc8USeQzEq830To9KkfvPW1hFxJl5rDZfhYqfQEGxcZwpY88NRplA0GS6sCOSfo5h8asAjpIYuC0EaAcOTxOjYTvLCCgYBIR2YEgZacC2kcWWJPnaxJplcuXLncxdlACXlWxdmzoJ3DIZHFGy8/cGH6F4kN1v33juYzGgDVbUV+K3lajABgB8oK593pUKDeRVfeOklvPblLwMANre3UGW5ehxH2N0lLsrO7i56DFecrlQQZ+e5I5QNFI+ODuByAdC/84u/AIfhkyCsWMWZKQpIXqSyPEMyY7n6yYndbIqFB6lNsWDy9PktrNdgyoKLxlj8syiMdSKeRhkmM5ZYxxkSTrWunb8Cv0qDMdfGBrcaBrUG9dVrX/ka/t3/9Bbdl1xBhdUdiYlwGtEXeFUfq1ygsdfvY6VNAUrVdXB0TAHKwXCCOvfJlh9iPIiXur+KZ6BZLefVQhyzzcD37x+jy9wo6TjYWKPvfPmVL84hqtNTfPg+1VIbDYZQTsl1SrDSIVjhlddex/Y284zCLp5/4Qa9Djz0+7RptppNrHHAI6VCGHJdp2iGjDdNjQymlGUWc2n8Uk18xuuFJuUnfzH/t88QVa3iYrVJY3BrpYb1DvEGHCXR4tphtYqLMUM8o+kMHis9rmwJqyr6YHeI6YzrSS1we57ZbXChFXlu+XSF1tZcM1MC7at04PA6Hesy7ing2t/6uwCA/oPbePg+yZs7zQCTEV3/LC0g+fqFZzA+ZBjovW8iLSXt/UsQzMO4fO5lCF7n0vEUtXUKTjSAGnOxPDeActmILc9tPa9lmsk06usEUfhuCyM2ntt9NAOX7cIXL3TgOSXnQ0GWqjHHwcEBrz07u3YuBp6y8GsaxygYilJSYOcBSZcn05lVt2kDpGywmSQ5usNS2VJgjflLnutaHsPB7gFUutxzlcIgNzTnZoVBXnKvFOyBbZiMMNynm635DTR5DAqhkLD7rskSGJ43roJ1gz6/sQGPD04f37oH2Fp5Ch3mwDx3ccseJr/xSz9nDx/VagU5y6jbYRUBr7me46HuLs9vmcW5PZxL5dm1vlYL0WTqgKM88D6Mi6steMwjU/EEDeaJqrVVVPm1iCfQGT1D4URoNGkuCqlwyo7zo12gwYapjWaVOhVAmnmYsQI29CU2N+jeN12FOGdbkFoT797aXfoeq2EVRZXWd51G1tjfgbRBi2cMXJ7vjoRVaxcCSJnuYJoNBC8S1K+1QdCiNTVLYxjm3kQoIHndnWUGE7ZtCB0H/somX08APmPANwLOKT3H7izGI45ALnkVC3d+VjuDtM7aWTtrZ+2snbWz9lPfnk5alhUUEVuxmymaPkVPtaqLlSYzyqVGxqe7JE2RsFomzgUSjrYyKORc+8JIx5IRCwhbEqKAi7x8bYSN3IUCNEeLaTTC/sOP6HqcGCesMKp3tuE/d4Pf7yEtlk9Php5r0/cvvPgivv71XwYAXLt63Z5stTHQDKX5gQuf077NlQ56fUrxj0Zja20fxzMkcZmd0Lb8wNpa21bEjZMZIi5FMZlOLUyS5wXu3CbfgrfeegtXrjwHAPjFX/olnDDpN42jZzIelEJCl1lgaORcWTlKphjP6PVglGLM8NMsMci5zkRjZd3WilAaNvMmJQCO0F03wEeHdFoOfBdffY76bZhGuHVAP9/06wBnbFVjE+tXCSLKez10Z/Se3eM+VvlkM9EKd/rLPcfANyjzX7H08cOHlKLfH2WYMumtopQlpEsp4HDW7eLly1hdoxPRaDBExqfjaq2KOmf7PC+wmcuVlRW88CJd+/raBvp9yvAdHx/j3kOC9TqtFVSZPKccF6tMqM6yCB2GMx4+fGTJ3Us1sZg7MXMFlPh0LdRiva1qxcMNhgoboWsVFzcurOLSZllxW6GwaXGFTp3G+OHpwFZ17zRCSJ5bg2mCOwwdi7kH6NL1vD6tmSSzKkzlKaTsp9S5egWVLepDN6hQih1AFEdY2T4HANi4+QJ2bpHx3GYnsD5L9c1t/PYfUfYxTMb48jkWEPQmSJjFqbw6bt/6S3p/fRVrHSJrVk0dfkiZnAIFcs6ANmpNjGPK0vSzCRxn+bl4ejyBp2jMJKGLLpdVyQYBNhv0jDY6azZDqBwXCffzvXv38ejRQ/4kbQnPcRzj4JAy3zrPUPAao6TCgM0G+4M+1tbpWRtozCZMAhcOphMmg5ocDc7+oVDgyhXoD8fIo+UYr4GimkkAEBWJHVOeo2BYJSaVQcJw1dgAsaF+zfMZLl6iDEClHuD++/cAAM9fvowLF+jn15+7CJfranW7xzg55XVWeWix31Kz2bR983x7BXFcrq0JZqXfme9aaEZJOTfJXaJJNwRY7OFVFHKG/yfRBA5DSFVXo8MZ7a12gEadMme3793HgwN+/kai3aB9dK1dx2qb1pu9g8cYTanzV1ZWEbKHTxon2Dn8AACQwUAyVK61g+6Q9pLQD9BkhWhnpYM6P89q1ce18xeWvsfW9WsY8T6Xz2II0D1KaVASDXwIOLwf+FJYNCiHsvXlnEAi5Dk6lQ4S/uus30XOqMyk3oTPEGQ8HoGTghgHVSsgqIHMbgEgEwbFiPpQywbyGvVhniQonKdnW5/628zEGB1Ruv/l6xdxhbkctTBExuZAaVHA8AIqMeeWhKGPjFVI0BqTmB7gcBzZm9Ba2I3VANYxcThNcTCk9x/s7+Jk7y59ThTh+tXLAIC9Xh2rl0kuvXHuvMWzBTRVD1yyzaZTfOnLBG/8o3/8Gzh34SJfs5gXJzXaunKennZxyIU1h+MJpgw/CZPDsFNmmswgeRHMi8IaPsVRhIgXzTRJLJcpLzT2mBcEI3D7NvESgqCCR48eAQDu3/0YhjdjXyn47jPwP7SxNb+KTGPG9YTGsyEmDEuMJzGihPkT6bwwXAnx8KVZlESZDDEHYP3jfVss7nbvFC/cIPinN0rQHVCf1Ftt9DKue1Pt2M37g4M9POCCfmjV8ZgX92I6wXC23CKrBJCzm+osE3jMBQinaWHlkbVazRpb7j1+jEuXif+1uhmiWqeNoNZoL3DHFjtwLrF2XR+rqwSZrqysoT8gLkKcxZgw9lxt+CjAypBGDR6ny0fjnu3P7XPb2N3bWer+yuuxHJ5F5ZNYqHFnsOAeOI88VpoBtla4ppnrQHEAvtoIUWeHYd/3bCAhhYDL11kNfJxyUL+x2kTK0EngnMDhvi10YSOdv06JryxJIEqX2LzAxgs3AQBOq4nClNYUGjHDEoXRKBiyTFJgi+dubgxSdmsdH+3i518gqHl8sIvWFj07pz7GLjv5AtqapE4nMdbXGHYO5psZhEHI5nuhG6BSoc1p7PlwiuVVWrpw0OtSf076Be6+T+Ongjqu3KANyYGCy1DHca+H3Yd0nb3THrJsvr6mJf9DSoyGBK1KCNR5w6tV66hywDaLIjRbvDHkGQ7ZYNMLm6hXSMp73N8HC90wMimGYw5069tQzeUCAkc6dGAF2V6UQzVKY2vG6boSwmPLD8+BdtnE1k/w/Gu08TVWOhge0zVefeEKXvwibZpKGvjs5PzVr7+K//t3qN5hd9hHp/0Vvr8c3S6t0a5XRYUNAD3fsw7W2nOtAgjGQD9DId8kNUjZ+mJju4mSMjoYjm0gJE2BQNAvrl6so0Q942wNfp1gKUhlVXd+xYPHEth6O4S2RZSbaLRorOVpivO7BG/d2T1EoksrFhff/5D2D9/LUHGZUnDrAC2G2ISJ4PI69A/+s8+/x87meZz4bJuhfIQMBlUBBNxXFaMh1ZwPamTpkg4kvN940oHjsbJaunjEh4xpnkPzGhNffhFjhnnTvQfQHAhpP0DCMUFRFDbQKgQANhgUnoEyNK6zNELBHMnPameQ1lk7a2ftrJ21s3bWfurbUzM86Udv4YbPJ9v9AU6H7wEAolYDCWdvhC6gODXv+B58JvblplWWu0Cc5/DZw6LjhpY8N5tOMWBzq9sPHmL/kBQ1uweHiBkuCZTAywwhXL18FbtHxLi/caGKtQ06mU8nfYDNroosRcCnmmXaysoK/slv/iYAYOvCOaTMoDd6rr047fawu0sR9HA4xozTjX/5l3+J2x+Thfn6WgtfeJFgtTyLofzStj639VJ0MS+TIZVCPaSIu9DApF5W9R7bk9vq6hr2Dyii39vdQYshFt/1rKHYMi1NC+sXk6cZxtxXs3RqiaeTaYa89DopyEsBANLpGKUHUQENh1OVaf8Qj99/BwBwvHMXklOej4+GGER0Ut1aPY/BlLJAu4c9rF+m0hut9Q3ceu99AMCPb93FxUukojno9tGb0hhI8gLbG+2l7k8qD10eF5PJNmImXMNom3HIsgzDAZ2sHz9+iGtHlD1otNcQsO+Dwdws8wlkRljUE8DcEyQvcgyYtPzw4UPs7lI2bjju2vIT164+jxduvgoAqDfOW9XPJBrj4HBvqfvjS1isyPKpxGWBeYZFA/DYULFTc2ExTUi0Qs7e+K4l4lLF8PKDhC3VUgkCxDwuar6LkDOLjiOhSuWG+URmZwHfehaEK0oiCJ9OvKtXr8Fh5UamNRRXJ4+FraVMECX/Y/vmSzj+iGCpVquBpE5jZ3/nMQZHNAa/8MrzlqTvei62zq/a19MpjY0kHmNS2tPnORSXFHEhEXGGdTaZQHGmRUrnmbyGWs2GrdXV682wt0eZiBvbbXRYFTiZTtFgiHs2meGQ4apqpYpKhdba6WRqyyQEFdfWgzPGxckxZ7cmA0iGkdqt0Jai2Hm8U6LRWNsyWK0SmV+JOh4+pr6qtH3bz5VcoN5Ybk3NjSpFZQjdKhKmI0SclJCGAAAgAElEQVRZAcmTKC+E9SAz0QTjKWVGG+sBNq5Qf2ujsbZFENVe7wDr4ExIECBP6Fltv7SKr00pO/+dP/gxjo4oI9Tr9yFEuQb04bMnU6US2P5r1mpAg0m/afpM9RcfPXqM9TXqj5dfu4qDLo0XqVy4pQdcOsNGi+eQTHF4StdcqVVxZYUy4LMogfAY+vEVmpxtFVkKU9aHUoAxXKMvELj2HGUo660KDrkM0unYQcAQXqu5gjrf42w6Q71D8+C0e4IkXx4VcGtNW9U9EAYNRmUu6wlWHOpbTwooOVdpMYcaiQEmjBDcMy4OHVpjMqFQ8ebiG8OZN6M8DJkKE4kAFa44r+MEGa9bOZT19kmjKWKGtMT0GEWFvjg510b6OZm6pwY8e/Dwr3/7fwMAxNMxqj4z0D3fyrd9KRFw+jVwXYScUqqGFdQ4EHr1pRdQ36ZU5Xff+wiP92kCz5IEXeZAZLMZmh0a4Be2t/Gf/8Z/DADY3FqHw46ivu/j5ecJivjx+x/h2z+gOjrbV2+iUqNJ64VrkO7y3IhGo4GE5ZfaaHhcbG08HOP+A4Ic9vYOkLEcsCgK/M7v/DsAwLf+7E9t8c1pHGN/nyC2b3zjl2w9Kd/z4HMtJQNj+Tx5nttJf3LSw5A3zlsf37aKlyzNIRkX3T04ssX/POXD8ZeTbAPAaDRBwYB8EqeYshog1YmFtGZRYV2dhZnjpft3P4aOSFEhlQOwidfpnffx6D0yfRud7KLi0WDcO4wxHNB93dy6gJuXaWH4/jsf4PwWLeiOJ/Gd71OwlMPHo4QW/dPTQ6yyu/IYCSpqOZWW0QJ33qPNTgYFjgYsazSFNVPMsxzTMW0EDx48wP0HxLfZPH/J2gCUfQ2wQd8TrJnyu4zlATx4dA9v/oAKzL795ts4PCW1WaVeQY2x+b3jLnYP6edbG1vWmPMPfvd38dH7FPT91m/9l0vd54Lt+ULx3vl/JeZyUGGAVo0LULaqVtVntEGT0+gQAgHP0bDiQVvliYTP89hxXWysUOCxUdHYZ8ipWqvAc2lMFVrC4CfhR2OezWtZBRXUzpOBXthu2U802kD4zAE0cs6ZEQKKr6d94Qqqm7SR+K6A69Mm4TXbmBzR3Prwx3ftAloLAzRXCFrItYHDDr/dk8fYukBO50Ib1Hhtmw5GmPHYF0pBMX9CAhYKXKYNJgOcY5PARubjxjUKvF+4eBMt3pwcT2I6pWtWQtv6UmkWoygLMaYxauGc2xHwJpenEo8PaH0dj08R1tiBvFrB0SnNi/sPT+DyOPRqNWxeIsjv+o0X8HCPVF2xGVso4nTUxSx+elHGsuVGg+M5KAX4vPGlqYbHEmMvCKEndH95kuD4kOb/2loDtWYpXRfYvkQB6aP7J4jL9TfNbN0woxy88hU6RB3cH+Jbb9GBvLYd4vWv0FhwDeDyRr/aWkVT0zNXU2XnvRHmmQprZtMUsk3jTmUujh5Tf9fqq7hxgQ57ZvAQl9dr9v2DPo0d1Qlx7wFxk9559x5efZ0Kw776ynXUygBAFigWLseUm7gUFpNZbVWQ8FgYxAoxS867UYrV5+jeV9faiHi8C2SossJrmTaNYjxXofcbpVFLaT5944VLOLdJe60wBqJ0Cl0w+y1yjd4xHUAHt3ZxqmlsRspBxjBiMptC8XxyVIDJCSnIkpNT+NvUh2mWwmOF3WjSQ79PAe3ocB+TA3p/MZ5hv0/j59qv/CJawZnx4Fk7a2ftrJ21s3bW/n/enprheflrv4zzVwmm+dH3/wy7d5khHo0xY2XNWGcYsEW0owMgoUiw6Gloji7f2t9BpUon3tD3UGj62uFwiNUap9Q3VrDCapnDnT382//j39D7XQ+VgCLESlhBs01ZoGB1A61LFB1vXfuSVXeYBQLlMi03GR7tPKS/nZ4iGdDJ487H9xE7nM5utSAURbAffXAL3/6LPwcAdDpNhJyGzAuBe/fpdPT64ItYXyFVQZ7nmE6pT057PRwdE2RzcHiIYyY/93sjTDhCT7MMLfZ/8QLf2rsXxrXRdO7ksLn5JdpoNAY0E9niDBP2uoizCFHCEFU2N5P0lILPpnjDgx0Md+hEUut0oDnd33t0F9MeZS48kWFrg6751t4J3mffCMfLMOOI/vKFVTQERf3f/vPvYsz5z0kUY+eYSbGdGhRDZmEQYLn8DjCBxoyf+Z2dQ0xYeaaUC7eEZiqhLZGws7OH777xHQBAs9nGl17/KgCg3mxDiNJHamEMGVj/jtPjI7z/AWVm3nrrTdz6iCDNXq9rM2czxBiwunHncA8/eIcyYcrxEDAx0c81ms+QiVSeM8/qyPkQd6SAU5Z/0bCVpAGD7RWuVN0MkTEJMvAcdJi8KoyxpG7X85CxeWcoDUL2nYJQqLHaZNuP8eEOZfva7RWEIZ36M50ApU/UAraljXkmqEA4jq0mb4yxxE1tDAS/dj1pn4zrOLayeTTqo8KGisgi+Pzzc+ur8JlQP5rMIBXNaakzZFydXGcZHP7b0fAIoy7N0ZX2JlJW+BQSkKzsq9eqGHPGo8iLZ6sZpjU8Jt1evnYVr13irI7xrGI1rFbQ71M/n5ycWq8v1/GscWWl4sEP6Hpm0xQJCw6SOMWEsydCGZsJHk4j3OZSFKNZhjZDINVmG5IhBBQGL1ynjMlwNsSEq8MnwQizeLlK26mJITj7X8gCirMxgefC5TUlmUwRlOMk8KH49ZUbzyFg+KM3i2w21BRAMqbVYNwboFJnhbDrQrLPjOcVmDKU/R++9SOEHfrbzY0Aisnmve4U7Qr1d6e+glpprqkNpFke7vnCjWsWGhueDKF5vRnNDnDt579EnxlMIXnt1mZuqpvGGT649SEA4GBvgG98nTJ8VTOE4YyHUgouX3NRFFbZKwrYEh9ZnCDk8bhWC/GlFwj5mE2n6IRs/Ogb5EwZaQcCGssb8sajMVZKtV01gM8ZHDfNoHLaqxxVgXDK2pMGovQXchwULOBwseA3JhU051iKJIXHa6RyUwzu0TrqOD4yhuKjeIaV0rT1w3cxukvvcbVGyjQROBKTCX3ObDKF1n8NSMszCTbPXQYAnP+n/wVSvsA8mWHGG1+SRIiY2v/db/4+khltXrnRdk9WroOYg590MsGYJ7OGAASlzaa5xv5RWWQzxm2WbEMKdFZIRXDluRfw/DlKN69duY5XWSmRpRPL4BbqWZLoQKXio1qmhrMMD5iH8Wj/EVpbpPqQmWuLwr3z1tuWG7G+vooGbx7RNMVwQNf/1ls/RJ0DuZOjY5yc0MLRHw6tqqsocgt7SenA442ZNh426BoOrFzSUY7dhLXJIZaPdxCluTU5jBONMQchUZqDoVMYY+Dzou+6EmHAKVtkmBxTYCZ1hIw3g+H+PlLmGUgJrLZoAfUCFzEP8N44wWqb0tKvXV7H4R4FTgfdY8x46HlhiIAhihsXO4iHXGvMAGLJm7znucgvUJr1QW+IvJxhgnD18vNKq4A8y/HgLkFaf/onfwiXeSmvfelnENY4jQtjlX+jQd9ytb733e/gvR+RcnE8niDlZzWajGEYAnCduWIBxqAksxVxhPGM+SFxgtYz1Lbx/Tlny4h5YOMJiQpDHloqawhacSUurNPYbDdCdAc0R9ebVdRYbqpMjgbXhKoaiaiEIvwAQ9DfxqnGhXMEwdRqBXJBi/VgltpFf5IUEGqutrRCN62f6fBRuLJ0owCUhHJLN2Og4EBYQduivloXSGclNy1FOuG1ZzqAZL6KLjRydry9fOMmmiGNk9FsjL2PqCaaKYDphOalr2aImaNXtDRcnn9JlqFIy0BaImMSjDHiCSXj57XrG9toV4mHsdW+AN/Q2nN4cIoBKxpznWPnEaXsDw8PrcLSUa6FxH3fRVQqX4cTu06kaYyCrQOKLEWW1/n6JcZstChdBaNKmxAXDx9TIHS89xgbm7TWnrt4AQ67yBd5CqWXs4goUCBnHiSyGA5vWL4TAGy+50FDsTJrNJ3XB7twac2q4pKJtMGz6zuQbGvSrLZhWP3kqBQeB1G+l+PSRTp0nXQj/PAHtNb8zC9egmzQOJU6wWnCBVFnp6hwwODDRydcW+r+AOC1L51DPGVzxekEr7xI6uVoMsbwhPoyzCdgg2Eo7WHGQRFUio01us7+scIGK7BkOkXK889zapYHlRuDtKQaFBo+9yeEglfaNtQrePkmBTxFoRHx3jwdDvE8w1vjNMUDdklfpo3SAmmfxkuuPIx5Yv77Ww+x9jE939AVCHi7dYWGdMtwQoLjb0Sqak0jL/aHOGLrliyaIOf9snd0gHSPxrs4dxngfUUoxzqF5+OhLRAu/h/23qzHsiw7D/v2dIY7x43IjIgcay5WV1e3u1tkk7JokhZoCrQISJBhU4YMwW/2kwE/+sGyXvxkQwb8CwybtGyDEmQJsECz2RxMsptssovdXV1TV84ZkTHe+Yx78MNeZ9+bdnXmTRCmpcRZLxV18w57Onuv/X1rfausgoSNVBF2iGLrxDF0tY4R+jxrKa3WWmuttdZaa+2lt2ciPHEkIUikDsYhohsd73SxN/a3FMYFDHn/f/C7/wceHd0DAEQyxd/4O38PAPD1v/aL0FT34/zsEc6PqXpx0kHaG9FvxUgJrq2qMuh9qCjGzlV/6+h2+zDk7dZ1BtDtWsQx+EbQ2YtgPJJzvPm6p8Z++OGHmMG34eAL762rh1uLim5Zx09OwBp4L5IBsi3KHIooim/90XdC/RtrXaBJhORBQyKKIsQUpCalCjc0zjmaqD/G2EbVa7fWBbJrunAbW2Q5LI2VNUBF0FutAbrEgXPfPsDXF+v1fF9iJWCIklvYGSYPvUZQVSywLoxiMCR4fdxTIXL/3dffwduv+gys80//BKck9x8jBSjTwoJjt+tvyENZI24ypuoanc52N+cT1YGlAL7Jao6CoOS6qLEgqlApFWoMSSGxmBPl9OABTp54nZOiyAP1usqXeP+7XrDum7/1W/jhBx7ZuDi7CBDGcDgMeilgDIrog8F40NRTgdZ1WPtFqeGIQkyMxPAFsiZSyqYCAHAGTvRmIhgGdIOKAFhCxW5eu46feMM/N+fnF0gpuPfa7giC1ukwiXHQpUBlVmGPbsI/PJ3hn/w2zbOx+Pd/+d8GAFz/8qvIqMTAycU8PGdScTiiBIy160xEzl6I0hK9FIxuiaXWAcViSq1LVzAesgnzeYaq8vuEcwCnIMt6cgpBKICK++CEAiweP4QbNvL9Co4WcJkvEBnKkNlJEFE2pJfIJ40xJQMikFuDOKGsJaGC7sw2dhAN0I2JxmIxjh95GODx0SmuHvq1d3GZYUHaVKbSGFL2llIKiwXpjwxTFNT3LJuGLMKy1iFjqywNTNWUzYlxSPvo2WSCFe2vDx89REb0Q10ucX45p7Gd4LXbVB2+uNy6XpiCDFm4xuYwRKMYqUI1c64cGNXzm88ypB2/7nb2JHKCh3uxRbxPQbyVhCD9nFgBFVXWdlyDNZuQAH765/0+fufuBJ997MML3ppcRYKmJBLQocxYXdVYEeKUyhSLeVNe4/lW1Q6PjzxquFys0CFRwb2d/jq4mzmcLv3vLssCl0tCQHWJXs/Pv2EzTJaUjRwnqKjvuihDDDBjDCWhXkVeoFz59eJg0SNx0GU+xb2H63poNSHZVVHgbObPWp/Ftf1+81AKSBJ4jJclYjrjM2swoOeyKxzSRgOMA4ZqXRlnUVLNt6WqUFHger2coiSNHb2aYEVrPCsrGNq3GBx4k2DX76I693uzmU2DnlKRlRvZzhK7O/65jwVHWTxbE+uZDk+i4nU6LkNIK/TVsxouRAdY8T/6+/8pHj32A9zrpnjnnS/RR3kQnbq5/yWwL38l/EYTge6cW9fB2Ei7dcYG5VCjc0havJESPgTff5hqd4GUZ7ffZD/84Qd4/31fg+cL7305bILT2QKGDirHTaj7YZ1DFgoTCpw88Quw2+0jz5taJTaoNCsZh0NRKRXEpYQQEAQrCyE2lD7XnXcbTpyDCfytMQ5VtT2nVdZ1SEtmYGEuGA/nMgRnQfm32+2i26VYgTiBJojU6Ayrqc9IKKsZKu0XL3MMXerXTlchou/vcIGrBNmeGeCrX/hZAMDBfALFfEbF5XQW6jM5ZpF0KOMv04jFdgAk66XgzRj3O2CXtOgdwGmYtK5ADBWiJMIhKfe+/c57uHnrTXo9wTFF///eN7+J3/k/vwEAuHPnDlZZk5HkAv3I+QqsoQF7KboH/sHrjkcwTbxVWUIXjUCcgKY+9ZMU7uHlVv0DgDiNQ4wVsK6tZqoSINpu2E3BjZ+T129eDdTPk7MpvvCKP+yGwwF2iIZNYwY+8nM+WRQ4pDivbDbDIxI4G3YU/uTPvILxq/t9qJC2z6CJL1dKhhgFB7ZmsRgLa3Yb09ZB0yVDcQ5Ojr/iEq5xLDlQLChGBQiZZflqFSji2nJkixNqQ4aMREPTtIvBDtWFq2r0KCtqsZiEwqNwJtSuQq3B6ZLXHQ1QN45rtgpzIWWEymx/kCxnBbo7jfAqgmPT63XR6frfOr9YhoK2SazQJWd3tVoFZ2ZycQFNsXh1ka03eiWhUqo1lqRYzv3F4uxyhn6H5r3Tw+MjP7+r+RyDvndshoeHWFCGo7IrSKJiWVVCJoOt+he7GEr4fhjGYSj2MS8zaDo0ecTQbWIzzlfh8KrtFI0c+3AgUVFh41gcYnrh57wzluBUJNTA4eSUaBcm0T/0Y/DmuIcn534+55MC+xQTqZ0BK+i3jEZOmaW5MhB8e/HI3//DD3HvM78PmtoioqXT6xp0On7t9JIOBuS1SM5RUqwRi4CYnK7jaYn/6Z/5LM9Rr4usqW+mNZq6wsaYcC5aa7GcE93DLUY7fl1UlcIjkiKoaxPWJgfDgsJNVMLw2ptvbN3H05PHiClkpOssEtqKs7JARcrGizRGkzCpHA9xfEWxQkFOXaYQCho/7HRRkMNWTS+wXPk1buMORBOnVK/PObHKoKj9rq6DYraxNsTuWW0wO/NhEHfVvQCa/DhrKa3WWmuttdZaa+2lt2ciPB21SamsEzA2dc8YY3B0o791+zZeI3qIORfKTwBufTt1daCiGFvriTg4ML6B2DSVxJlrLrBgUgZax6M6n4fkrMtbbGNlvsBv/ONfAwAU2QJXD72E+XTmMKWMrWW2wKopWW80KgqM0rVFo51++uQ0RNOPx2NI8kCViiEpqFFKGWo4ASygOr7adENXrTUhHHMh6NNauy4hwBAyHrYyLiBkU4reQdCYS8fX2T5ShnICURSFulPdVIFZf5MsszkyisQrsyU0icF1u3uQDeQsZKACTx4ewbzm18Nbb/0EdOnHZH5yhgPKEuknAjlpbCzrOpTMEIKjzLYLlHRwYHTrG17bw2ribw5lVaGpJyHAQv2xd9/5Av69v/urAICv/tRPhQrExlp88MEfAQC++Y3fxieUgVVXVeiTg0VFiI0FQ/+6v8nc/Mob6N/0gY/OrGFlk2UoqCK2ZcsgPd9zCUq+PYwOtvEMMYT6Zs4pXFIQehRJvHnVt0HaDBdTvx73RmPsUp2e/b0hroz8lfRffuuHeOsVj3TNsgq5pKBWWCyoHMq1UYLP7vhA/j/+4w4yCnzVtoZpEEhn0SgAcs4C2uqce6FSE44LsCaTp6wgmj1AArwJ3Cw0TBMRuaFHdPzdP8D5fZ9Fap1D7egGWOfQuR/n4Zuv+ghlAHGnF27anf4OSt2UUpHopn58umkPgrIwV6sMugGrrIWmPcxhvQdsY0XN0SQ8xXGB/UOPODEwTBb+pjqZTnFJiR2dKIY+83RFVVUBXVwtV7CU1WOKEgsS1ayjCENCrtIkQkXPqAIHJ8SmE8d4daOuUqMZNd4bwdJz6cqTkD01SLvQ6tk358ZErBAR0qa1RVbRDb1al5momcO88HN4uZjhxuu+LblmKAtKjDEWJSFzu+M+au1//+xohp0bREvpHCtCc8dXerAEQ3QHHK+/5ZMlynmFLgW7LuolmKV6elZhNSWtpg4Auf0c3j9+jLKpFRUpTJd+Pzg5X4Szypg5InomVNpBSnRrt8fhrD9XTOXwyQMfuGu1RJkTQqIXELKhxHVY40IqgPu+OKfR69JeyRIsKTbBunWdvThKAEY00GKK8eH2YRDlxQRS+TWYO4Oa0OLEGPCJbzNTMmhicaXACT/J8wJ25eel5jFK+mwFh2JBiUlVGfZmxiUYo1AGAbClR77d8T1IQgg1EDKzOOehRltdFigp5OLJ8TEEf6ZL82yHJ43E525YfC0764e2yYpx2geGwNfjSQju91tg0zn+Y2NsNp2ZAIQ7rOv0YJ3m6v+7IQzXFBsNH9rOerFAvvCbxW/8+v8ISWmxT84mOLv0r6+y3Ds3ABbLLDgqn/7oU3S7pJZsLHYoZV4qBSkaRdr1EIunKJrNtq/TDR2wFnFyFobSw5w1iJrFJQArXkDDlgsI1YyhDUKREDr8lOAyQI++rdR+BXDihbjlWMz8JstLiwFtgtdfeR2fPfaLdDorkfSJBuj10CeazMg+3v/YiwOenB8BBCdb5lDRnK5WJfpd37aIK5h6Ozqk002gG/hdShy87YXUnqjHyM5owy0tBsT1/ru/8iv4+s/8m76Ng+Faz88avPaaL9b6+htv4NE9f9BP6wqmSWEFEBHttvv6dex9xTt0vevjEANltA0ZRjxSqBtap6rAiRbZFTFO3PYbkIpUeA6EFDDkJFrGwYlOPCs0rpBK9ygHfvpnfLr97PwhlG0ygBx+7be96OPHnzzA2/v+cL97PEc68G07WVRYkMNWao28iV87PsJk4R0eZ+26lhZ3ISvKOBecaMbFCwm6Rb00ZL1BaySUQm5rg4KKIxpdQaU0to5jQc7k8tEnKCnrIysd0hHFSRRTxElDhzHMSaSss3MISSm1nV4XjKixbmcXjur72UiES5szNgjeVboOc+qcC/F621hpBC7O/RgK1kVCKrHG1ZjO/DP08NEjfEb19FytMRh4Z3VvbzfQfEW2gCFRtny+CnvVSa5x9cB7VLduHoT05vFohBnVHbu8uERKsgO6rnF+7sdttujg1o7fz8qsxoQyqW6/so+0sx2ldb64gCBnU0kXLsP9ThcFram6BuhuiGs39xD3/AOYly7ERDoNdEidf7E4Q639d54/WqIu/fuv3zrAjx77S4noV1gtKZMv0hj0ycmZGyzomVD9GGWjJl8bdLqMxr5Enm//LN64eRM/+vgeACpETMeodhFieu6NQBD7lCoJ6dJ1vY5rM6aGpD20MDrQ42nUW196Xb3elxlHTfRWN1boEJfGeIRV5Z36JI4CtatUgib0ZHf3WrjYbWOxMBBNiIkT4M2FWdSAI2qpAJp0LMMcJF2qpBMoLjxl6iRHTuNT1ybEkkJbyOZsq2vwtJF3KQGKA2YnD6AoA8smcYjrtVgr6DttQ4xenCSIxbOlPlpKq7XWWmuttdZae+ntOUHLa0prE57+f1JaCFQLf0ryfi2Uxp4qBBTk7zeonOY36I+QlfFUlofDBsKzDm7yX7f5/u39uIRxpHsklc0sSoKAI8lhKMrVWgtBVNR4vLNGsJ0LwVMqjjYCj/FUdtUad7JrpGtj3JxzARq0Rq/1KmIJRZHysBYJwaVJxIFk+yA7Ltb0meQcUTP+YHBEzzHG1oGYTCAlpCuKJY5mVMtnZ4y7FKTdh0NKQZCdwQhHP/TBvnE6QkHik5YJTCf+sydHDwBFAXQ9YEk3qlKXWDY3AMbgKHCvYx2Y2Q5mtrWBq5vAPocOaQLdeOM2lkNCAM5nkIRsHZ09wQVps6g0CdlAzhr0+/6z+4dX0d/zdIOJGCRRfHE3RXfH97s77qFLN2i+rME66+B0QxlmOopCyQrrBPLcZx3MHj1Cara/VSaddTuttdA0NNbUqIsGvbE4nftb9Js3OYYEbJgkCroYv/lnn+B3vvsJAOCNcQcrQgkeP5ni1cjfqB9PC9SNSOPlMqCtedEPWX1PiR9KEWgs5lyoSs85awDfrayqa/CmOrkQqAjFSuIkIC1W55CJv6lmkwku730MADi+/zFKokkcs8imHsmRpsb4phdPLcsK80tPGzGVoiC9j/lsgSu7rwAA9g7fhiM6wWzU5eHJujq54CzA8VYbVBTQvo2dz5a4mPjbbxoNkcQeFXYyRw3/fMyXBXKa4MnlAkek47U7uwgieq60AeVbZhVmpAuzqGsUxt+EBWMYU/CoczzQMDV3EKTVo6sMBe152bzEVDUlHDjGhF4nSuHs+Hy7DkqBmhZJXVUQURNAa+Eo0cKtgPnC7xGVLRGRwmheufCsMCfQ7TXlTYCSEMprh3uB7rs8usRi7v++fbgLSSgQh0OfMr+KuEZZ+Qchc1OAalfVQoecpUgk4HIjC/I5tntlF0tCOs/PLtDIMMUugiEEQxsDUA0vZwtwQvermiOibM48y9aBuNoG4ck4VgERsnBQlNxiHUIJlyRR6BFKx4QAIsostC4Ajs5pWNuEj2w8sFvYYHqJx43ulGVII6qXKU0QGnZcQhGdzpxBh9ocJd1QpsiAAxQSEccKJZ1DuqwAqtXF5hN0I/93dnaMksZWCYErOz7gfCUjzOeeClRKQTTMhJSBjeBcQKXPRnie4/Cs/9nH2IA6txnPsz7QgfVBz9xG/A9zeCrda8Oawntug65ijgXGx204OYwjxFIALIid+VY02VsqZIxsY0pKJMTlM6bRowNvd3eMN97w9MYyK5BTZpbWJggDVlW55hI3FB6dcxvO2FqHNVISsQoSiSHThrH1gQrnYCijSikeXDduNcCaWj4ldJRt30duQ3ZbV8Qhmj6OIuwN/YI6vZxC0nx1VIyaNqe7j0/xaOozXl4b3sD98yk1X+Fd5qUJTu48wvvf+5HvozShkOjZdIJ7j71QIWc1qsbZkw5a+O/PtENFKeVORagsbXhlCbWlwzO9c4aMBB21dehRhlmsIkREf6MDiecAACAASURBVOzc2sOIsgs+evgx5v/CHy4HhwehJlFdVZhRVsvp4hzX3/XCk1d1jYioOaZkyE4SgoM1lMe0hqr9BlRyQFCxRScFZI9iReDA6GFGfQJTbr9OozgKMUjWmvVFRDOwpkovSmR0SDPrsJj4eRNRivtn3vH7g4/uQdNaWFUGZ3TYHM+WOFn4NZjZtURBpk2Ip7NGIyMPprYmQPZePmFNKdsNOYcXkYioyhKSxllJiVj6v8uqQkpz9+nvfgOzB97JWc4uUS/9gaeXczjhx19KBkFbm5AcgtKhi6wGJ1mIPK8xm/i5Fuoqbr/h6b/h/nXETa0xy1GQQygiFcQ7Ta1haBxsXQXHYxvb2e3jMaWif/u730ZKhY67VyxqyroyWuHw0O89+1dvQVMdI8YLPCBBwqo0SOhQr2OBkp5XJTVu3PLP5e54gGzmX5/O5igo5qc/7GCvT89ZR2FQ+rm7dWMHVeHbNisKdFL/PaeXOb7/0cOt+leYOhQe5sxB08Gn6woRrZEkGmJJde3SQYwOZWzltYZm1CddQZumiCsPSvpRX+ONPR+nVhY1XnvbO3QHN4frosHWgBNVeM4rzEhAr3dgYQXNG73Pt22FiOjTbcwy4OoNf0nmEcPxQ7/HCXCUeeOocNR0y8htjS45J1wylNTOoqrgmueYsyBZIiWHq5vYQ0BS3JaxLoi0CsHC3gYhoJpiqbV5Kvs3if36EoqHfWsbU0qBkQirdhwLOr+Vs5BEGwk4MAqVcHWBYkK0WteipjNmvlpB0/PBGEdBO8KKS7i+d/bZcoqSHGBX1mAN5WcdhuR0l6sMhva5KOKQJLMRxVGQy1FK4nnhLC2l1VprrbXWWmutvfT2HOHBTeEwjkBdbdzb+KaY/AaasVnhgfHNT6x1cjhbo2yOsY3YPxu0Yvw/NpkbdShBb7QLwVxaG5REaazyMkBc25jjHODr32raaa1BRK/v9BIMm2rExkCKxssWMA30yDYCj63zkCZ8RHkTcc+sDxIEgLquwzhaZ2EIlbLOIsubKrIcgrC7OKmR1f6WPs0mqFbb1bbxfXKIG0/c5gFavnb9BhxlLZSZhiMK5HwxQU6ZTifzCbrG/25/VmFMSNQPzzWG9Pp0+j2cPrgHAHjt1UO8+roPGj7sShjnPfelznE219R+hjnB3rlBuGl1uIPVjbCk12/axrSp0aNbeV5p2IAkCQyasiHOwBKq5JTB+dyjH5f5xRq9YQx13YiaMYwPPfpljEVOsL8Eh9xoVxPMrJIkoJ4xY5iTyGLJLDQFaGfZCiDKRlmD+kUCepMYEY29Lqu1nhNnKAjdKvIVHCFOs6IOZUz4oIM/+8zrYxV1AUGQegaGJVGIS13haOn7mDuEchuADYKNaaxQz4mudA5yI6uyuTs54Clq9wXyB2B0HYIyFeMoSipd4iym93wG1qPv/REuqURJlKSoCCXLCqDfJwSs1EHIrJMqXJ74mm/OOiQ9f+NlfYms8hP25ttfC/Xr0k5KwZ7+WQ103mIZ0OWyrFFR23RdBERrG7ucTDAnGub0eA5LRXGuFTtY5R71+PSDs1CSodft4RZVkN/fv4HTExLs1Dk6pM/DTQlJAb5JKnDjps+8u3wyx8kpIVRcIKYg5J2+RDdpbuYWNe098+kcKlDrHAsKAj5fGnx4fzvNqFLX4BS0LPlGtq01iIkWOTldYUkie8NxD8JSFpJVeEiUuZElEtLnikQERvUXkwjYST0yUFUOvV3/fHd6AknZ1J+qoOl3ez2F08eeQjx45TrqRkiq4uC0H1SmgHPbhwh0e3tQsX++mYjAKRPq6OEREu7nwWogipoMW4M0bQRKXah43+unsEFjB4GutNYEgV0pWdDkUUqgS2xEIg0aUoBHAob7sR0l3bBO40SE2muMsYD8bGNWa8Sl39/LyqDRSNWMAxTGYSSHVo2+UAKWUgCzEAGpTfu7oY/nJ0+wXPk1LsZ7cBRcjZN7AeGBlCEBAnBBnHC1XEA0QeBcIKZ+JWkK3gwEQ/itH2fPprT4mvdjsE/F3jS7u7Wb8TxrSssf+o1Q3kZNHQZYWuDaWpRED5WVhTPr1Fb6E8aYUIujquugalprGyghZxGydGqrQ9bY15/ZdW91WcE13+MMQiK9cyHNvPl/wMcrNPwq5+tUZwEL08TkMAsmmgh3Bk79resqfKeUHF2CpMtiiVKQErLV6Cn/8DmukROcPckyzKnGT17VkNvXnQQHA2/UoSVHL/Upm3WuYYlH7SVdnBaep5/nc2QEfxcWWHFK8z4YIKKaNuVFju+f+EPojes7+Mmf9qmlf+X1d1Brf8AMEo0qo+yKzGJOxWIulxoz4n5lwtGlBRs5gwWleCoRo94yp3l3fxcjEpQ7PZ+EM3Y8HCGmw32erQLM2h8N1kUndYWKXt/tj8Cp8F6mK2j6JhlLjCjTR9c6ZJIIIUNWkVAqrBEFBn3RpO+vUJNAmNEVInL0JBhKvl3/AJ/12GyCXCoIulGoSIailpwDllSULwuDFakir6ZLHFFqcyQVGEHko2EHOcWlVA5Y0VqwbK0gLgB0Y6q7BIa6OZykCLEFxphA7QouN7K0fnxG5ueZBgu1cMoiB7GbmH72A9z5s98FAMxOHkKRGnaa9lFSIcMk4Tg994dytarRo/iPIufo7HnRxWI5RU5UimUWw6F/fffKVQwpg09IgZJoLGcdFK2Typqw+RZlEeL4yqp4Iarg/GwOS/E542EP4x1PjSDWmFz4OdrZ7Yb4uJPHx/jgAxJr06+hQ30vsgqa4j/imAW5Cwfg+Nh/z2qikZMDL6XD4VXfx/39IS6oIGksBBLKArtz7zjUmzOaI6divzVPULrtDkuRA5r2GhOt47Ckc1iQaN7Dh3N0SNQuXwEnpFpc6BKMLgdXRqPgkDh4kVcAYEzi6MTPw907D3D1hp/n3pAjIecx4QyM9pSb1yUWF0TJ5jHiyLenKA1KcvQEF3AvUJxwd7SHR0d+HR0fXWBFawpCgJGqMLhGv9eIt4qn6Oj+wDtsaapQUDxMmnSxILp7uVqik1IFgiRBQRIRQghcO/B7dyJZcHgzbXA2822I4kEQ39MmX9dVs3ghIOB6J8UOUXIaDpqtBRub9PAy1zi58HM3WyxDlhmXUch2VmkvZFEZa0N1AT2/CHFNLl95CREAbDNujjM8uO8vatoapHR5jZMkiOQqJdcSMFKF5/XHWUtptdZaa6211lprL7092x1idci4sJYHuL82awrJGJ8XD3htjkZrhsGhaKrtWhdugIxhrYWgTcjuqIwLwUpKcnCCywXn0CsKxOQiQKQbIBP9W9Mh8WMECT/fsjxDh+BpwdblLTwE+P/2iJ1zT0H2DY1lrf5cjSCj16iUQwElGqQoAodHb8r6GLOSqs87h0vSFlnlGUwI2FZBkyfpcMTx9ndnAQFOkPDVK1dhiQZ4dHQS6pws6wIz0A3WVWiE5KTj2E88jDo2McqVv/XdSiVeu+VFGm/u7WA4juj9OR6TzgjbT7Ag8bDLpcU88/O+zHUQrYuFCAJnpqxDtpUTCtmWN+fuIIWjW+WVKzuwjSifseCEDu7uDlG7JjuNQzXlIZI4IJG51kE/CVLBuSaoHKFqNmIFE7K61gGFUskNyqOAoRpe2f0jOOPpkmjQAagqty3Ni7A9UJIhoRud4GsRLmtKcNvQbRqG0N1JvsCyobc0IOi2HHOJkvp1tS/BqC5RpBDE12LF0e00mkIOewPfx4ssx4RosjQSkKJBXlkIeLdufYuyTgREaBvLFwsUUx8AOti/jtMTf7t7/Ce/Caupj0kKEOTNoh6iob8xLi7P0B3fBgAk/RJ66bOxLqYFzqdeTylVIuwx+SLHjbd89la3kyChCvLOuadq6AW227GwrrJaY0k1gYzZEGDcwnb3BhAksjabTADu9wCdOMQ0v2/dfBWcINzZ5SVOzzzC8/Gde2HPy3QGNfb0iakFFO2j169fQUXIni4vwQgxs9bi4tQnHKRpH7SlIq80cqLKZ5mBkhTwP1JBfFJXBXa20x1ER41x79QjvNMiQ03zZiqDOiN00CXgVDBptcyQF0S1jXcQUTkJMzew9B4mZaAWSw4siFY1nKE/9EHLeVbDUrJE7HyQLgCkHYYrVzya9OjuGW6949eyLSyqVaPDY8Ozvo3dv/sJTi99H+uqCmfbYDRGTHToYjaBkoQcD9Iwb0Iw7B+QOKgEtKGg9W4Pq6Xvl5DXsbfr9wwHICNhPcYcuiSwyq0BKKON5zWmlKXHmYSktaNtHsR8JRdBWHYb49ahS2OIKAkB+8zXmAIAzBdzzCb+OVucnYbnwDiOit6T9gdh7iqGIMLLVivYpmRUngcNPqf1BkvEsKTSEnGng5hEQFUSI6UAbxEpCELfI6Wei2I90+H56ME0/O1L1BMVZUw4JOBYOJSdb6XvqJLBsXGMwbkmRmXDHfF5dr7TG+KErlqLlwnmfwOg1GnqD8fa2ai1DWlwcAwZ0SXb2GIxw5BK0FvOwDczT0IzN7OuxEacj12nD1oTal9Z45r1ASk1ZBwClVBXTdaCRk00nJYppnO/0c+yHEvizkvtAnQXMYaIDqR+yqFfoHio5FEQU0ujEbLcx690+jFyGqvC6TDm3HEkNA43BgMMqT7Mg5MlNIm17Y9S7A0pVVEkKClb5uDaDsZ9n/Fykt3BvPAbQ14ABaUNK8kx7DcplQ6GnCLjZMiw0kyHNfM8S4XzglUAro52kDUZAlmNmNp7uDvGJcVGFdpgd+Th/Z3+ADPa2JdFji7BwWkUYZH5jWZVVTA0HsIBEdEHsZBI6PsrXW84IQ6MHINuWYdnxRU13Dk5lZbD8O0B1r2rPRxeoc2L5SioJlC20ri88M7VcE/AkTPADTDVfk5YYvC19/zGejkxWOa+PeMxR02ZfzevJ4hpAeyMHG4zDx9ro3GTKLnFosL4kFLsoxSH+1RMMzeoiKZ5cqKxe8VvaqsV8OR0+wymy8d3MXn4IQDgjZ/5ZZze8dlYk7PT4LiqNMVwx2cPRVEXNVEgfdEN9eWOH96BJud6Nq+wCorvDFcHlEk3N3j1PU8PMcaxojXg695tCL3ppvBrEagu7UyA4GutUdfbx3+kKUKtq7NLjTz3a79zpYPDmz72DUbgk4983/d2BxiN/YHdG+7iz9//HgBgkWXoU1YgKiCv/Vq9ASDPKW4uW0LSwXNl7yoYXeyqGrhJSstpxMFIjuB0f+5rswHYvzoKwpJJ1MfR8d2t+ve9jx7inGKUNNbhBUY7CFIJltyioOK9nTjFaNfTesZoTC/9Wu5HEleu+tfnqyUmFBNXOQ1FGTrDUS8UrS1yG2L+okSF8AJd1xiN/bPOZhrZogmh0Njpeaciz3OsVttnvQrGcOPQz1W1E7R2EcdpULHPizniiM7CyKFLxZgjlUBQ2+KYo6Z4HhFFuPHKK/SeGJpCCvKsDCrEcQT0SO6iqqtQo9FOKlxXniaL0l5Ie6+qEZa0L1vnIMX2Dg/jPNC22pi1KjIcKjozpvNFyE5mnMPS82c35DY0W66/U2tUoXbmRr72hjo0FzwACkpKRI08SppCkWK2iteVAEQUhXGIvUruM/vVUlqttdZaa6211tpLb+wpYb/WWmuttdZaa621l9BahKe11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvprXV4WmuttdZaa621l95ah6e11lprrbXWWnvpTT7rHx/eveM49z6R1hrN32VZoqxqAECtDYwxAADnHMCY/7DznwEAWAcO/7oxxr8PQBRFiOMYAJBlK9S6AgAIwcBhAQDMORTZij5bw1n/W1wySCnD71rr3885h6P2/1u/9LfZFmPgmvb862iMsef28X/45o+cpXEDEMY/W67A4V8XsHDk/0oZAzR31tSgaacZ2fwORm1AWBvOOjDO6P0WTAgAAJcRKu2/QQgJWN8GrTWSJKHflQC1p1hOcPTZ9wAA/9V//p88s4+/+jd+yQlqezdJESV+XQjO0E87AIBBt4dIUnt5vbFOHYT0bTS0/nwbBQS1XVsL62x4//qZAKpK02cNHK08JhUK7fuh6zqsR6MNylKH32im7r/7tX/83Dm898F/42R54r8n2sFqNQEAXN0dY3Dtq75pMkFY/YyB0d+6nKAup/S6Qdy94v/mPTRzKBRHaCgswHzf8/kljh9+CgDY238bg/HQ/xYHivNj6liOZO82je3n36EE+4Xn9vHo5J871TTfGGjjxyrTBazzHz97NMW/+Ge/CwD45je+jXpRAAB+8etfwzuHO745ukLJ/b7SG/Qw7Pm/T06OMZkvAQAXswXOLmcAgNHODhj3a+bJ2SVymmotI6xoHkUcYTD0fY+iGKtV7oeQccSxX2O//j//xnP7+N//o//aTSd+Hvd2+uh3/Gd1bfDo6BQAsMxz7Ix9X269chuz+RwAUOY5lPH77ng8wI1brwMA5ktgnmcAgKuHYxw99N//5PFj6NK/HsUJRrtXAQC7V65iNjkDAOTLBSbTSwBAv9dBkfvxsVUJSWtexTF+9hf+OgDgy3/tbz6zj//Ff/ZzrqzoPLAG12/eBAC896Wfx41X3/W/09/FfOl/5/f/8Pdx955/zk1+Bqn9GDCbo8h827nag439Z7u9fSzmfuw//NED1GUJAKi0Q7ejfP/GCf6Nd/16tFZD0TnBuQzPn7UGQvnXtTaw8Ov9v/yH/+1z5/A//kfvO6X826QSuDif0FjWiCK/l412erh6pQcAuLKXYm/s1+Cgy1HT2Xk2tc02C6k44tgvflMbPHpIZx4MStqXYtUPe5gSBmni2zwadNDr+r+HA4Vux/erLDTmSz+HVWVgjP/+X/2p3ef28Z/80//VNeer/7xvs2AWHdpTj89n6A5GAIBEAjH3866UDGc/Yyzsl4wxCOffI4XCvCY/IO2jWC38e0z91PujKALgz5s8p2dOCRSlb89skqEo/N/Xrh8GX+Rv/a1f+dw+tghPa6211lprrbX20tszEZ6iyKAi7zVbY1DXdEOXEin3npesNGpN6I02zYURnHEIujJaayF4g8ZIbIISRZHT6w5x7L+Tc4a68je3sijX6IHjAGuugEBde89Oax2+M45j8OeDHk/ZFiDJS2QsgBuMMwi6yXPn/w0AYCtEDZ4jHArjbxhcJXB2Pf7OrtdDA5JxwQJQwJwLiJyuq/Ae4xDQBOscLH2ngwPoBjs7P0Y2O9uqRxwMsfJrJ5IKgjdrIUJZ+rYv7AKdjr9lKbVGpBjnEHS7S+IOSrox1pWG5rbpCAz1g4HBEApV1wbNnYExC137W40SHILWqWVAXVc0BgaOkIqqqsH59uvu7MkZuvA3yYdnj7HKPLoib43Au/6mz3sdCOvb4LAGsWytUUyfAACEShFJPw6zZYFK+78PbowC8uccwIXv++npBI8fnvvxTA/QGfbRvGkxvfB/Z+fgqUckVHf4ue0nsOyZdnR8hKS5jTMW1ohzDpcXHqH63379N/HJD+4DAH7mvS/i9t4+AKDLHR4/+gwAMNwZoTfyt+uL+/fwaOrXUSQZ0k4KABigxp/eeQgA+H55H1/50ju+j1whz/3eU9UFOCHQDBxV5tcmNIcp6eZsNFaz4vmdI5ucn6Hb8yjAeDxGWdA+V2t0+77NUSfBcuFRnXw1x3jYBQAcZ3M45vfjo5MLjA88erKzdw2K2uycwWrhx0rAQMa0f8NC09qYzaY4PfFISl0V6Pf9nB5eP8Ddu3cBAIbVGPb97zIuMJ1Pt+qfrobo9H27Xr11E6+/9SYA4Obt25CRRwY++vQhPv7syPfpTMKK9/xvxjkKeLRJmDms8giPkAmgrgEApnUE3vXtvXJdYnbh53Z+fITFyqNGpe7i9m0/HqOOQoNuS5nAOv8sGmthCWHQxsGs4c3nWn/Qw3Do50rrCoy2ibpr0e36Mev2FHZ2/NoZ78QgUASWOZR0jjJuEdH8qEiAgG6wNMLeFf96bXVomzMcgtE+5DiUavYwBkbQhXMGBHLAOQvGA2QKLrbfb6y1MKZhTRiah9FtnBN1XePs1K+jfqqwO/J9F4KHvWTzbDXGrBkaoQJKczE7QUUI5d7OAJL2TrbB3DjnQnuMSuBoQ+n2HHodP3C9bg/GPbuPz3R4qrqAsWtoKgxGbcOBEUUCkgZSC/ZUR5X0k6aNgda0WcBBE0ycF3nokLE2QG5KSsA1E2thmvcYEw4zazYoBMYguB8AIQT+daao/r+xDQ9jwzppGg5m7gwY/S1gwQiyny+nQOof3KjTXX+LW1Njfi2sH4iwVEwN1lBBQsHSU2kdAyMnwzm3sbYYDH1nWayg5BanJIA0ThDTWmPOwRB1VgsN0FqrnINr4FTF0Ov5TTMSKjhifl36tkRREuBRzl3ws40xwVGJNhw9Awsmw64D7hpK1oS/nalhre8TYyys/W0s4hUygn3L4hyTBUHeWqEuvEPCkyvQNMaCA4ZgZZ3n4WAVVQFmFtTmAR4+jOgXGHav0oEbCWjtO5ZXBkXp/748v8DVawf+t8CQ0xrR80vwiXeohlEn7A2wBrRHQXWe38dPv/dhoJasZXCOaLWsxHe+8z4A4Pxijl/6xa8DAL52+xb4yvfrj779HcxpnP/qz/48FufeGbv78Ucop5662hv20I399+9cHePL1q+Zf/577+P9j7wT9d5bbyMhSrSscjjWbLhzvP6W7/ubb76FLPO/WxRlcMa2sU6vh97AO10iSjBr6JCsxCuvvgoA0LrGRx9+AABYzOdollW2WKIq/drr9mM8fvwIAHBD9HDj5isAgM8++xDTC38IHeztwhAteHIxAZZ+3ueLJdKEKL9uAkPjNl9m0M0DLhRkQg4PHL77vh//n/vlv/fM/o16HXzp618DAPQHIzx54um10SDF+dSvlz/44x9iMveOh1QCzaWh0hwV3wMAOL4PJ4ji0RW483PlagtLi0olHXDh5yqJI8TkPJw8ucCf/OknAICvfvlVjGi8JThc8yzChe/R2sK+wOVjkWlU9AxJySFj//1pquDoMlTZElnm+1gkNdK0ec4UauPXtYoYJO1bSnEIQfR4bcHoOeZchHuocSasTcEEYrocRBEHp8tZVdfBAXNWgLEmjIAH+mwbc3DrizFjYY/mjAWqHwDm5JjrUmCPLhmc86fOhqd8h+b7mUBB4QD3Hz4GyM/YHQ+BH3N+N+f9cjKBrfz+NxA1UkVn/5Ih+TEXrsZaSqu11lprrbXWWnvp7ZkIT21M8MgE54jIoxRCBvg+r+vgwSnBoVQTZGRRET1gdIGKYH1rLCzdup02cHQbhzEBfrNao0EMJBfhlqWiNTzJIULQsnUmtEFIFpCi1rwxrOkBMDztuW/QjvOFv22iyqAoEGyZLTDoHPr3CBmCRze98KeCmZkLyMjl8XGgqISK0R9SgFun/9SNobl1WWsBuvH0Bn1cHG1HFUghQ2CiAseSvH8uOTi1M45jdLv+JpaVGZaZv22mzkEQIhFLDqC5vYj1OMFA0P94tJFowA2qSzCEm5g29Xp4jF4HQzvraVn/Py+E8HT4Emelb3OsFrhGF5kiz6HPvuv7ko5hol0AgNUlcprD+uJ9JBQNnGcO3FEg994YUeQb/Tvf+FP83C98EQBwsN/BbEpowPkDPLrnkYTF5Qn2dynAm/WwmnoKQeoKZ0f+Rj2bz9ElykHBYln5+XztJ57fx/un5+Fm6xxDAw7OZ0vERAN95eYtDDqePvvgg7swFLy9qjK8+vaXAQC94RV85//6lm9z5ZCOPDLjUgU0VHxZ4Es3/PdcfOE2vvNDT29dZhUkBaQysYby41iik9I6iSw6PT+GXPRw/fbo+Z0jSzp9zAlpMYwj6fs2aKwwofFczKbgRF0VWYEZoTRVXmCV+UGJU44HDz39tFrW2Nv1yMiw10En8Z9NE4lu17dttlwFymc4GmO84xdQkefIaZ8WUkERWnE+m6Ob+L+vHR7iZEsUiyW9QCNf3H+A6cL/vSru4LO7Hnl6cjmHooBhUwGgZ7cyDqI5kqxATmeDdgbS+u9x2vnQCf8m1DQ2470rWNG49ropHh95asxYjZ/6K37xJQlD88hpbcO5oq0FnkOFbNr55QrW+DFjHOhEvs29NEVCgcQp5+Dcz4MUa1zBOIApep0buNr3pSwtZNwkirCQ4FHX9UbTDIT06y4SHIqQFgcT9hvGJSyNobEsUOhCyLAvb2MM7HNDPXyCCgVsSxno0Fh4FMm/hz3FJwSqfONVbV2Tt4KsLEP4C5gIKNnTv8uwmHuk9u7738bQegQ37dXQtT8n6u4Bxtfepk/8O5/br2c6PLPFIgxSGsXoNNk0XAQo0W4cWPkyQ9ilnIGlxcgEDw5PVVSBZjC1gWKSvpMFvIlLrOM6XB0GSps1jyeEgKa2GatBjBZ0WQYYtzVvvJqiedKlkKgr/7ByONC+g+Vihid3/KFVLOdQzv9DdzBEr/KHKLSBEx4KB2PgjuYXxjtVAAARNo+qLJEt/YFUlAbDpV+wb7z1DkCOwmp6jPnML97d/RuIej6DqNO/AhE9G55szBkDRwtg//AAUeZ/ZzKbIKLX0zTF/oE/+C6ml5hMJtQli8BJVzrQQEqpNaUFDUWwqeQiUDZcsLCZaa3DJUDXZk3fWROeD4aNzEUwcLE9wPrgZIYJUQKDDtDzZxEqo1HMfOxKdLmHSvn4ibyYQVGsTjY7RkH0gFIdSOEdIVZHeP21LwAA3v/uR/j4Bz/w/apT3H/sqQhZPEBGMSF1YXDx0M8J51fA6JkurcNywUP8FgAAIABJREFUTgfMdIlrYw9t9xOFPNveqbvIs+B8wloK9gJqVI2fgkcPj/HBke/v+aMTxNy34WB/iL/66lsAgMuzGX7wyT0AwJ37D9AfjQEAb75yE0b7L+rUGbj1jvH1gcIHND4nZye4etU7IUmiwIlPipTE+RNaV5ffQ/Bb+TqD7+8/m+3xbT6/wJLW555z2D/wsSm9QYQHn90B4B3v3bF/5spsGahDCY7xoKFiGeqq2Ucz/Pl3vIM3nZzDUcxdvspCPJJ1FsPhwH9/pOBoj3RuHduRdju4Qs7HarkMDvOTswtEyRacJADDuzi7IOq1qjFb+e/44OMHmC39vgMGxLTxJEqB0XMjxDoupXYMwvnPGq1DvKAxFra50DIgpuAYa0w4M4aDAUqKDX3w6Aydnqfm+l8beOcGtLRY4zAA6wl9vmltg+MkGYOg50xJhSTy39lPIgzISe9043A+lbVG7RrqysFlFJYRpeDS97eyDqahUiEg6cOW1SE+RzOfQQt4+rpxKkQsg4NUOQNr/P84x1C/ABCwSUU97fhsODMbVJcQPFxWGJ4KtQ03bH+9ppAUB3C63HZ7fTi6FDLOfOAj9T4wrJwjX3n6jJ9/jD33AAAwXq4gab3HvQMcDp/p0rSUVmuttdZaa6219vLbM90hznm4tWZZFlACryNAnl0UBzpBcoay8t5WXebrDB1rUdYNGmDRxIcJycAV3X4Z8wFa8N5q47EaYwL0r7UOAVPGIFBX1hmIkPVjQ1bM/1+2GTT9r0IG2L0//98D5G2dxfFDD98zpzGkQLOiWIJl/gbmlhbnOd0A+U30pv4WpZIEUd/fPJ21kNa/P19NURR+zEejPcQE5e71LB5e+IBaVhjEdKvUy8eYL/wt8PizP8WKEB4U76J3xWd1xN0Brh5c3ap/Ko5RU1B8ksb44k3/HX/47T9CUdBvah2C/Pb6A+T0+2WWIWuyOGCf0oha024WjBO6KdZZhg6AbKB56yAkZRmaKtw2OVi4oTlr1wgG40GvaBv74MEqZDTe3mNIdgixUw4FrXdx/iky7m/02llQfCNOpxWGKQWJpg5LQrdcPcNwx/fr9jWJ1cxTDmcnET6+51GIK3IBKZoA7ApV5ufq2niMOWVJnlc1uj26XccFuPWvFyWg3PYIz2qZrf/HODCai7qqQkbN0cML3P3Io0/CqpDB9As/+XP44k/+LADgt37rd/Bnn3q05Gy6gFz6NfAoq3CFNHkSl2OH+/WrnEVNWSKVPYMb+PXbGexgSOiQ4TbsQ3VeQTf94ghU5jbGTA3CSCGNxvmxz1YSaq2hJDaCPg/39yEp2J4bC0Zb9pPLY9RNkoHLcXTvIwBAtsrR6VBWV16CN1plXMBxv36YrnFJVBMTHENCk5QAkPq+j3YGMIUfn1WeYftZVDgnfaPZMkdOa3axyGANIaOMwTIKidBl0GJLZRKSKDRjkJS0UOkCVaPt4xCQVyHEWsdttUIc+7Vc5nMIQuaUUfj0zmMAXmfoYKcXvofIBZ+IwJ6NDGxapHhATtJOhA5R5TKSoK0PXAkIGnsGEc6qsrKoiiaMQ0ATytSTBo4Q8Mql0LbRAGMB8DDGQmv/fEvmENH4dJIoUFqx5qBhRl2vz1Fr1gkEfyFjLHyPdQhZUcYGaTUIw5qcI3C+br9zLiA8fvD9PtHrdlBRgDd3FViDwzCgpnnRsBhQ1p7szjHOPKI8qHKoBm3TSyR29szmP3OWVaQQEd+YrzJUxM1KKRFRrI4GQ0VOTuUMNG2CzDnU9HpRVeD0PdathemcYDAEpzprYXL/gEkhA21grQ3p5z4Dy7dN6zpwhg4uCMB5PvMvPrNPZw+9mDHG8K9SptjFZz9CduQfemstYAnmNgXMjOBSayBojoRj4I0IpB3i9MhvpvPsFCmlXdb5AhFBzsvVArOZdyDG4z0MSIxK5zOYC7+hjzpDDAiCvf+Du7igLKN6dQZGm8H9j9+HePAjAIBKuhgNt6O0tLNoos3iVOEKHYKp5FhRrE5W5LBE5XU4R0JzW5k6wPu1W8sbGMsCvcW5CLFLSsoQO+Yfftp8ZQIHP34xkxDU1yhSIeagrOrATpdahw1iG+tHBgN6sktjUZKDF9UO3ZRi61QFAUpzzh1KynzIbQdDSsddlSsk1j+79dklUP4QADBOByhy/zw9Ossgqyn114BRq3dHQxzskkMoZ2DCb0DOlsgz/1sxGE4m/rd6HYcDlWzdx6LQQVKCA8Hh0QaB3hr2e/jq124AAAb9g0A1Hr76NkqKe9k9vIEbr3und3nnHlalb//J5QyTC78Gxgmwd8sf9KNEYW/k41sG4yt49aZPdV/UZXBmyroO4qkWPFBFxjmEnOAtbLw7hi6Jj4QL1OdquUJF+1wnibG359t2uDtCPvGbu15lWFCc3eL8BJbisla69uEEALjlQZqgykpIcggs4ygL/x4dRZhN/EWkrGq88sYb/rOCYUVOjtUVQNTL3niEwXC7OKXFssQso1ikxRLd1M8/w5qCMcbAkJQJ5xZRQxdLBkXCeoXWIT7Hwa1j4jayQ621G7QLh6Qwi1VdISYBQOsEJpRJ9IMP7mB54CnzOFJgTRYo4yFjaxsb9hMwWmudboKYQj04HBSJB6qYhbUJxkJmsq4ZWN1IgSjo2M+/WT7C5KGnatObX4ajo7moa3BytK2x4cB2ck05FZVFkwNWFCbER9Ua4dk1GzTcX8zWwYq1sUGAOBIKjC52hjPoECfq4MiJrZkJMVqe/iL5kCSGq+i81zVck8ELBqOabC8LZfw8xmwJTiKHqB1AzqFxDIV9dpxSS2m11lprrbXWWmsvvT0T4bHWQTS6HkLAuiYos0ZBNwHj1oGYui4hiXKKNkSOlBTBy7OOw5BHdnxyiZykvg8O9gP8HYlqTRv8GHrLWhcCuIzRIbKb8bV+wLb2eRTUpk6Ks1jrBRkDTTeVzRuGc3hKC6gpV8A5+9zgr79MqmvUX2dOOK2Rxv625hEduiXmKzga0PFOBFN5xCZyDBcXnsZ4dP8z7JLgFtcZFCj4jvEgSHixOENFkfvWVpDNbSC3OHvib6rTZYYVIQLc1YiEvyHpLIfQfl0ViwlOptsJDy4WS0SxH+/ZfIZFz993qqpCRuv0YnKJ0zPfrqujUUAHAQSaNFFRKHMRKYWa1rWxDMuVR6Rms0WYuyRJkFJpgJ2dHVhHlKCuoCh4EQ4AZXFIJcMzBNh1bN4W9u4ti6wifZi6xNGJH+9rewyjId3KbY4LCnDNC4MOBfSyskBG9I2zHN1dogEqDlX5RlwZdfDhA48A2IIhJU2QVCW4se/HaponKCu6sdcaYwpkHckUjnRAmKnRH601tHZ4d+s+CiEDwmO1CWvKI2y+Pa/fuoZEeQTmu99/gOMnXv/nD7/9e/jKu98EAPyH/8HfxT/8B/8AAPC//MY/xb/8zW8AAKrVHBHN6Vv7V/Duqx4pqosc+7u+7+MrI+zv+DYvjxe4pLVvpIAjjpBLFSgNYwL4tJUZMCQUaJtneRBkHQ66SAmNgdXIqJzOpauwOPEik4vzc6xIi2m0t4OIqJSzy0uUtD73dnYx7Pj1P18sYBrk2wHTmYf7hzs7qCjjryhK3PnMlw6ZzWc4uH7d99EZ7I79PnF4eA1JZ7ug5cVSh4wuV2foJ41wo4UxTaaugaUQhziON6hdh6qmdWpcQAx8ZlCz769LFVRludbVcSxQSEKumYBqvoCjOV/MVzihUgX7ezuwTXmkun6hjMlr1/bgXIP++gw+30eNTuL70u8pRFETeKzh7EZJmcDYuLCHZvc/wMWxR+FfufkeItqXS2bhmvGxLqw1rmRIenCMQROyMV8tg4CvswxqQwh4M0vqL2QNOl5VmFHmVMT6QUMtZw61aOhZG7SANPRaI6iuQ6Dy+elpKHWyf+UqBGXkMQCWSsQIbsJ8SYtwrjAwWEL3uXMw9bMRnmc7PLpG3cDxeQFNP1iVJQo6QCFkWHSdJA4LZ1VWiKJGRKwOucuOKVxcesjzez/4EV555S0awx7Kguq4cB0ivpWS4Turaq3Wy9haXZKxtUOyyX+/qDHGAj/86NEjzGiDsGZN23EuMKOMlKoqsbfnY2O0tk3iEcUj0cIfdPHmmx4y3qzP9JdplasAggaX2RKKhAS1M0hTv2nuDq8GZ0Zyhzz3D9+guxPEzhbn5xiMKDU37gToNDcMGQnb1XWJYuE3UyMdOGUwFHkOEG8vmQQjMbvaMES0QQvnYCkzhDELW23nEUglg5N7cnKKDj1sk/kcixVlrBQ5dinDRUmFJTlCZaXRJ4XbQZogSRo1ZhVEwTTjSFb+QDw7PcWcahtZV6IKDxgP8QQOLNSBIs1j/35YRE2wmRKo3LMfzk370ZEGiQR79dgmpbPQyPPGocpxPvVtG8YMvYT6XgKzCdE9QwdtKM4HCPF33Zhjr+O/9O7jJbpUm6c/Vlgu/N/3H2V466anWt66nSIuGwesRqfXOPUddFUT67euO7aNCbB1jJPcpBQ58tz35dr126hKP87379/BfOH3DO4qzM58DBLqGu+961PUl4sCJw/9Wj66+zG6zK+1t28eQNMzfX4xC8riia3QpUyhXjfCnFRxo04aDuZaa9Ih8M90JNTWfZzP5yAWA7qqoI13VHrDHVxvsggvTvDk2EsBzLiFJNX50aCLfp/iRZIIFS0CU9boUvxKP4lgSz8mCjWWtFYXWRbiKHUiceO6l5rIihILGsPVbIp65J2c1XyGcdd/5/TyDNN73tH6mb/+d57Zv6IosSLnKxI2OKpSItTk0/U6TEFKsXGJtevUaeMAS3RPWYTsXO14UPyvixU4XaSl6oS1o2SMqvRtKKs8XFCsc5gu/HM8HPTX8UJav5DDk8QCmuLm4iSCJHqlE3MM+n5t9nsyCPI6Z1ERx6Mch6XfNdxA0b5iJ0cY0EXN1gVAKsQKIsQJVkLA0JwzAYio6S+HbFSXmYNjDc2uIJq9x+KFKxB8nm1e1MuyxMW5p0YTgUALCsGCYyO0gWjqNeoanOaXWQGb+bX5yfe/jxWFG3zx3S+FcArmLCxrLpE5JImMxtqC6w0F5ua4tw6a6NQfZy2l1VprrbXWWmutvfT2TIRneXkasrSqUq+DnhwLAYUONtA32tpw02cMqOgDEQekpBs9r/GEavAssgJdEmpCcUoqVBSZbpuqtpstEqH8gYOBI9eOCx4yvOBePGC48e7v3bsXbiff+ta3cHbmvde6WmfvaK0xpawirxuhwu82l5PFYoHBwGtedHsJ3n7bo1ivvfYa3nnH1+wZjUZ/aRRXtWK+QjkAVDE0CfM5ViB2/kbX6Sj0KTulzCsMqGZSf9DD/Uc+q+vqTg/7VC+l1+0g6fsMlo/uP0aXZOg5d/i/2XuzXsuS60zsi4g9n+meO2fmzbEqa67iTIqD1CTUUqO7BXcb6Ae3nwzbsNxvercBQ3/CL274QVAbRgP2Q8O20KIkmy1KFM2qYrEGVmVWZuVQdx7PPcOeI8IPsfbaJ9mszJNoGzCIuwCiLpLn7LOH2BEr1vqGjCp12mpMKSufzXIWdPNDH5JAwEZK1AR+ltYwOFjqGl6wWDXMVeYIeFxV2N5zLQAVRtgcOKbX7vbn2KXdyDSdIQyIGZLEzApQyoM1DYheII7dNeXaIEncv29eugxFlZ88y1DQzuT45AQDAnaGkUI9twNpnm1VVNz/UAKQzwDYzcf+uEJ25H7r6hC4vB7RObc2L1Up4RMTZ5ZKxEQZSWcepu6Ro64MYnJTXuoY6NJ9/jA/QUGMkeMZ0I8cYLzUNT7ZcbvQ7/3WDbxy21UYZJ2jFO7vWhho+i6URkmVuXFh4PnuufQWuEZfSkgq22qr5/zOBDo9d283rr6EfObu4dXrV3FK1gy+Mfitr30DAPDaSy8xmymJe+hSC+nKpUsISHtnlhXYIV2dWVogJtHTSApEfstUOstdZcNECo3gpK0q2KoFy4rnAC1Dl+iRMKPxFMZjsghJpzAEZpY6R0I2CcpU3ELwwggljfN7Dz9nUbmV4RDdZZqHyhzb1BoJwgAr9B4PhwOsX3IVpBoCO0euSp3OUsTUxlXSw97jh+63fB+ffOLICp7nI+50F7q8Ss+zc1tBPM9XDEjO0pKFS+uqwmzqdvpRoFBVTRsZYE3asuT2shEedwuMLmGoTdPvL3HVRXp+67NYVeB9vVDIqLI7meXoNaU2Y59rzcjzAkI0/lwSjWtEJ5ZIiEAQeKol1QjANABdY2CoIqSFBMYkQpkdwkvcfCqm+yiPHBMx7K0B4U13XSLmdVRKoCmjVLoGWAxQtT6FQrJViLG/zlzoi2N+OZq/N87TihhzxrRaeFrzM/W1gCJWZSgMwxRQZWwdA89CEdMqPd5BHbjxVVYaqqkC6RK1oS5RkSKgaliUV1zhcdWypr1loao5pueviacnPOMRLwYOZd4wetoHGPiKfbKmWYaQeqTGWufSCMALEy5t3r33GT66615IYWJGuCtVoKQynlTeXIJRPTGhsEmasa2gmxX8Yllrnqs8CQAHB25wffTRR45+D+CHP/whUmL4VGV7zMlkggnRB1dWhtxHVcKDocVyf38f6+uODTAYdPHBB+8DANbW1vDVr34VAPC9730Xt24508fGcO7/q/jmm1e53ZJlmXs2AJQXII7cQBPSR5MTBQEQJK5VN81zXNlybYzNvo8lYl0cjib40d/8LQDg/ucH7M0zWOoioRmg14mw1CFVX99DQq2rOIwwjtx9rmTLJEiCAEHiJujEk8z4e1YYY2BsQ1WVyAqXZH3569/AtduvAwD+5//pX+GUWpHj2QTXrhDTJwhxRImQJyU6hFUodQE/boTADGyD20kSZqr5YQhDbc8iz3FGf4uxRUB9fU8pJHQPirJoZ3EhuMWwSNTWcuvkfBTCFu5e9ld6jBcyM4N05u5DXvkYn7pz2D2YoqDFIJ1mODh25/PSrQTDrnsOhWeghbv3mwOFu4/dvXr3lzNMiNr/+isW49yN95WOBy9sAAIKhWm89SqUJZk+hiWsXHyaTZKkVWUNLCc/aZ6i13EbiEF3iMd33UKs8xIdSlyXul3cvOlax0L5rLp7enqMo2OHDzg72seQ2oI5SozGLjFPayCh9kAnLTA8cgswAgFTN63Xij2tlGo3XrCC36dFohP5iOl5DZb6WF9yIodFWWFy4vBIWTZFTGNGiRDHNK5Gj7cB312vTJYQkQzCbDYFMmrtmZJ9iYZL63jldTf+D09OcUASEaWxKAmeAGFhCZNYliUMjc+t69fxcNsxLHf2DnDtxmJzlFBtO7+uaqYhR2GECd1vo0tm1ZalhU/t9qpKGBuqvJB5zlKAlc5h6zmVd42Y1KA95TBRgGNeNnghWItaN+ziiOUi0jRHLJsWtHmuZ9i1E0hKeDrWoN+0haVETBuaqFKwZbM5BxStH5HWLKJYiwCTmVt7THaIJjFbMiOMTtyaoao19AduLvY7vdY8VFl4XrOpalZjh9Vh0WLTuiBYab/Qo+rXhbX/fqLT/Newx6BkpeUmaQbcfGypvZVnE2Dmxq9X51CU9HqBjx5tPhKdYqZbk3LT+G5WJeqa8FFlCUvzZWAAH41kCBjj5Hk+QvX0tf+ipXURF3ERF3ERF3ERv/Hx1ApPnIQMxK21RkngOQEJj3R4qjJnBlAYBOyOHfoeZ/ppWuDOZw8BAH/1t3+HweoNAMBSd4CP7jwAALz+0hADsnmXoq0a1bVmRLy1ds7CoAVEWmuZKWbt83kUAcDjz51Mdb/fx1/+pWN0FEXBoFylBGupzFKBAYFfu90uX6OUPotXVVXFbtyDpX7LKihLvP322wCA0egc3/n2dwAAL7/yMjY3HfPkVzV8/t9odb12+1or7y3AolDGCCjaMUL5XAXQtmZNkB/+6Ce4/fpb7ji3LuPxQ7fr+4u//THevffIXbvwEQfu/h+fTeFT1SuKPHz9TaeH8ve//RUkJGqmpEJNVYOiKnnHEAQefKoUwQrWfXpWCCG4RRUEART9zq3bL+HGy85HZ/3SJezWbgdYZCmaclZR1djedS0wPwyAgIDKVY2UxlQQRewPpY2FoO8mYYyzc9KGiBMuu89mKZrije8LTAmcBysgGqApDIrnoPdMJgLLodtll5WP3YycxFUPaU0st1rD0h7G8z3UHkn2dxQi2nGNxgd47wNX8Xi0nWB1hXyAkgpnpPfyYHuCO4/O6T5YfP93rgMALl/uwqOKoJcEyKn9u304gqaqQj/ROCUmkbICceMJsUAY07aOhRAIaSxUdcUVyr3Pt/E3P3JsrMPtR7xrPT/28NEdpyn0wiu3kZDmzJ27H2OPWE5FOsGw11RUSkxmrppQyQCaWEDj0QTad9feW+mjR5W6cwH4TMKwbG9gDHg3u0jEnQ5y8q7yhcCg557p/s5jiMqNkyJLcbTv3oko7qEmJiu0QRCRuKVSbNczOj7AwcS12S+tDvDqK85PKOkNcOeuY2B9tr3NujPLq6uI6FqUFPCocjgZT9kW5nw0Ykf4Tm+ApeHqQtcXhiHPWZ7yGLyfqNb7EBCoG2CqMIBw1aw0Tee81OwcS7ad6z1hkRFsAnUJRQo0k/F5O3/pCrph+ojWe0sIDUvHz/MCZdz4oZnnYjBVuz+HpUm09hVmNN8FUjZYdvhKtJpSc63aedYuZID61OmOhcLg8Ny9l9PDR4gr926le2eIeq4t30XKFTPHRm5ZjBINo+1JVnBDknlyHdl85jUa07bK57WPnHggvaOyreYZa6EbZpm00NS22915BD12Y/PFy5uozxtWl0aH3umu0sip7SWNZraXrmtUoDZlWXF13IOC4jalnNPBEtD107sCz6Cl15zYRHHAPck4Thg1P8syBIQQt6ZkKqHVFgW1FmaZwIcf0+LoD7G67krPnowwyVyZNatqhEVDZVMIqDXmB3NGpXnOtDPPaxlPBU0g7t8XV8wEnChiREwlXWvG5Ny6dasVVCxL/rvWBT/8KIr4pZRCQVJ/MggCHghhGCGgRdT3ffh+Q9P08PbbzvTx/HyCjc1V+t2bWFtzA3x+kP6HJEFu4LY0/3mlazMnTNX4oMWeB48GVJYW+LM/dwvM6de+jLd/9gsAwLsfforabzxkBLOGfOWxh9r5tMQ77zt/rm+99SpuXHW+QVZryH5b7m2S2KoquDxpITk5eFY4RpX7XrfbhSal3zTPkZeNfIJFp+9wKZPpBGNarEMvID8t4OH2NrdhoyhESuqfPSERBHTMLOPfPT074/egOxgwnqcoamSkpmqsayMCwHQ6QUjUZi8Inse+B4EOkY2IZeMHiMmnJytKXsiiJEa32whDpqitu5bB6jLfS6ksLLG0Dg+n+PQhiYNWFuekvjuZaeTUg/ek4RL89WtDeFGfjqMxO3HHOTtJ+ZizqMSDY3ecjSTBcrz4WJ1nMGqt+b0XQnBS/PDBp1juu2u/+vU3cHbmJtPDk1P8+CdunPZX+/jmN74FADg+OmBxyDCOUaM1J73diBNmJQ5Hrp3UsUD/nCjevR7UhkuQPpUlDM1JMGCsmbUO37hozOoaPmGrdJri4SPXnjve/hS//40vAwAePBrj0a5rdWxcSdAjfFwcKIxpjJ0c7cGn9sbm2gC9q+48r2ysc+vokzt3MSKBxCDpICLWlfIkfHrPYj/kxEZYw7igk+NjxhdtbF3D1vVbC12fNYbxgtIDcoIpdI1FEDSCcjWzf6UwyAhGoISAVMTYNHbO+DIgdX+g0AUq2njrukJKybWGREVrg2O/EcNPtNAHYzQZwgKzvESfxnUvUHgOwiTu332fu0NSSShK6pX04DXq16I1JfaUYrkWISX8hk2mMwyse87LiYcx4dFOdh8gtjRn7G9jnzaf/a0zpGT8q6RsPf2EB6+ReBZtoiVkK8YolZx7v9545jUaWEjdioBWtmnDGTQ9M4sJqtK9f9NMo26kPmwOnZJq+/EOCsL03V69inOSpDHTY7wQuGu02vB6qeoCNPWgNgaVbRKeDKYxILcWFU2ewviQhB80VYXZ9OkmtxctrYu4iIu4iIu4iIv4jY+nlkOEAJce67pGQG2DqioZ9OZJgZJK+aEfIAgbIN2M5bR3dkbYOyQk/tI6akvIQeNjiUrMfhSjIk2KuirmSp5gQaluN+ZKS1FU3Maat7231kI/R0tr/9HnrNNx86Xb+MM//MP2OHTcsiz5t6bTKbMXut0u6/AUeYmaAI6zWet7VJYZA5udzoT7TJ6VePTIVb0uX7kE33fVj6OjY2acSSm50hVF0RPVq/nS/7PiSUuOmisqUkneRVVVzdmvrVqA+ku3X8Zf/Mm/BgB8+mAfadMG0JJZApCtf5mwkhkmxng4Hbnd2//6b/4t1of/DACwvtxjoSzP81i+35iaz8fzgsU9inSFwGv0L3rMFFxeWkFA92c2GWN85rL/rfVNRFRODeIICblI7+0fIkzcjsX3fSQEYBbC5/uXpmPEkbu+6eycKwazPONqnx+GmFAba5LNkJC1ufElZqQzVE1SBqYuEh0/wMmYvJCkQdyIXAYBPHovoyRhTYoiL9i2IIh9GNIuSToJAtJs8b0Cvtecf4WG/uKw5Y02fIBPPnU7zwfbOV5+ybGlTOCjINuI9esbqEeuqnBn+wxe7a7r2mqf21KLxGDQZ0+0vGgrqdITDKifzk7h+e4zK2tdWNtUt4bIa3ctn3z0C/SoXTGbTZhFGvg+egM339y6/BJurTktmg/f+QU0AYY3Ix9dGpvDUKGiitz9yTmaBqvwA3ZIV0Kx3soikWY1eWUAy7HHlbfVQR9eA2wVAlfXV+jHNFC7eTHuDzCmqvnysIsXr19zf/c7OKG23aPtfewfuN11aYBb1NK1SuDzzx18AHHMVZilpSFKSdUQCPg0lkqroGjuCZOELSqeFZ7y0BQSylJjRtWjcDJFTmKHAhY+6atURQGdk2Cd1YgIBKu15m5AJZu9AAAgAElEQVRBnRcMSE6zGc/Fvd4AYexarNoK1NQSrPIUAc25MgpYwLIybZusthopzdEdL2Bm2SIxNQaiaSFpOfdd01Z4pIVH758n1RNzd1Pt8fIz9Lq0ngUdTCs3P3WKDJr0ulJIZGTPY4e3kNlmbVDcihSwkKRxpqRivSghBP/ufHtrkbAA3zdhNdsIiVojpHkimGxD77o2sr9yGYPMVQHzfILqwNlkrOkJJr5bI7NpgXFK835p4Es3r5S1RhW7e1XYElqTVURdQTdmPKZksUxTpVCCzse0fn1efg5UT38Xn5rwlFWFgCa1OApQU8lQVxUDQYwxCGmB84OQS5hpqXF07DABn95/DEssjm53AKWaRf8M8TK9SFXN3w18n+luVhgoKk/mdQbBpWTxxKKv5+lxz9HyGR2e4u2//ak756pEQu0tNddzDoKAB4tSitteSZJgbc2xsVzvu/3MhMwpf/nxB7h82U2st27dgqDyW55VfJpLSwNudQB4IpEbjUb8b03ys7Kygm53MZoo4F6O5v649gCVeCsDQSXkMAwhmlJlXcKjk3vx1lV89S1XAr2/e4ascoNUVlUrEodWMKw0Fo2FTF0W6MUN/bvT+g/pmBcMWxmwMJ/RLZVT18wYeVYoCVZTnU6m+OZvfRcA8L3f+T5GhEtZW15mxthyt4spCVR1e10MiCnz6PEuHjxweK619TV4lESNxxNmvpRlhtUVNynPshwnZxP63XMMh+44nW7CG4JpZjAlY0ptLYu8PSHeuUB4ng8/cTe20+/Cpzbp8uoSt7HKyjI+pKg1P0/la269wQhkqTu3SVox1khCQFLylhuNtCG5SGDvyJ3/j3/6GLeuEytDBTjad2Ph9LhCP24wURJvvurG+8ZKgEouPsl6PiAaKYIo5Fa1mVbIS3ffrF9hWjscwPHUIKL21jdefgOd2CWu777zIf78z/43AMD+6ZjZc1uXN/Hyiw7fcmtzHZZMLuXJGC/AvfdDKPSuuwlaXukiICbMshTYb5IxJVATIzO24PG+SGxuXIWlzUE9PeHErM59fPiLj+g+hFjtu7Gk4hCXtpz6sVASPl1vf7CEJcIJfnbnU/zsJ38HwG3Obt127M/Lm5vM0sqyGQS1HZUADLUWzs7GjPtyrzAtHnECkFJ30uthRKy3Z8VweYiDY7dpKIoSOY21MPBRERss9HxsXiWmox/y5mc0mmB6Ti1jlYDnBS3YEy9Lc2ZaJf0+vMAdJx3PWPW3l/iIA/c8J2enPM9OsxoEHYQ1hsdXHcvnwmHNsjHLHhjtWGEAAGlZzDBQAgGtH9LMrVVSwKNzK2dHyI5dSyvq9fHowM1VWV4wXrbSNYsZptuP0agDhGEMQd59EuAk0/f9uVaXhPEaQ+7nhEJYgVI1CsY1BK1Dy6LCgMbFsBfga2+4sZZ4EfqnbgNfCQ+edPdkIyixW5Ba+d2/wzolgcPAIDxz4poecqTKjYGprOFTT0vokjfA1lpkxOzT+QyBT5gfAd7U+jVY7uSL4qKldREXcREXcREXcRG/8fHUCo+UPjpdt2vSlYHWLssOo4SFAT1foaKd+M7uEfYPXSb4eGcfe/uk+1AIRJETVVpeGqKuScp8sotTEnG7OtxCQGXWuqoZXGZ03WokaMOiUxI+6w1IKbkaM8/YWiSksTgjHZaT0xOcNfpCQYAr5CsTBAFXMMbjMdsxFEWBO3fvAHDMNYG2fNiUTsMwYIuKO3fuuFYNACV9rhSdnp2ykNVwOMQSybunaco6RVEUYY8E9R4+fIjXXn8NALBOAOenRV1VzCASopXsL6sKVpAruh+6UgmcJ4wwLoMOhMXvf98Juv3lTz6CoVJiWWfc0YJokfvOW8Zd1wuX+vjym26HefvFG4ioCpDnM24zVMbwjk0KO2fPoWGe4YvShO95jNoXQnCbMR9PcHzoyvtf+epXEdN5nZ8cY0KS+0VeIKTnYK3FdOrGZhAE8Gls+stLLIgmlY/1S25cDIZrOPqbnwAATo8OUVPJ9dLmZayvuPfmWrKFO/dcK+FoOkJGVZ28KDCZA0A/K/LaIqDzDwOPy/1B4DO/pJxrNVe1dY7XcO9H03au0xQFtYXPxhnveGsNpOQqPs4szkkw0kgPJbUc/vW/eR8v3XZj88ZGiE9JT+v/+PNPkJA20Q9+8Aoenrvjv7+Xs3vxf/6VBS5SWG7/Bp7k3VhH+cjJEmD96hX8/d/9xwCAzZVrSMmmJoTBoOvuydHBPj65/zEA4Pz8gFvx169dwuqyq5zoosKd95zWyfjwEIOeu7f9W5ew9LJrFRllsEqVtJvCQzpzY0b6HipiZHpaQDxHO6TXHaLKXYvK1CnGh6Q7U1XICfycxB2skIXE1os3sbTuGJy/+PgTPHjwEAAwSzOcU4Uq9iNcv+UA2JPJBJbG7Y1bL2JCLuoPH3zGlaVBtwuh3PFPRzOckypl0lti77nR+RgekRKMBfZJq+zZYeE1rNSqAmzrSK6ouu1HPrpxo1MFaKqMh2GI8cSNuyxPGQwuTIiI3M87UYzaNmBgHzm1r8/ODqAr93c3WkUzkfhegD5VRYQoUI6pdGk0avpuUcjnavc83t+dszLy2GbEUS2aCo+HsJnrbdsVAIA4ogr7LMdffvCB+xsaBa2jnU7E85CQEteuufnm1ZevQdWNaGEBwW0sCUlwCk/XfC1SSsiiYROa57Q1kshpTdL5BNWOq94MMUMUNuVf19IDAF1ZjEikU1VTJGPX0vJPtxHV7ndTKyAaIHc/galJ2HdSQEauU2Jrw/O+LQugAfgbAJ6bY7J4DaeyYb4aRFRiVcJy3vBFEPunJjxra2ucPIxmY5SEA9C14Bfp4OgQd+44at29zx4hCh0TRogYgnxraiPY0yNLZ5hRH/r85Ayn21TGGx3jq2859lYn8WEaHx0BKCq/WiM5+dHQT7S0mnbP80YhDWrCMYwnE05m4m4H9x64heqXv/yYTf5efvklKPr8+fmI74+nPMbezPdOpRKQTSm8LOdogpJLjEopxImbXI5OzvCjH/0YAHD37h1sbTlsz3/yz/85tq4Qzuf0BD9/z7Gl/sHv/d6zr7EsGE/led4cbd/y+VR1xcKJtc5hSYHXUwIvX3ctip39TXz86Yd0zoZFKS1anJXVNV580T3H//Qffg8eIR9OJzOcn7v7EK/0WShwPjkNPY9bL0K0AlfPCs/3YZpWappyDztLU1bOvnnjJmISRDyMQhzTePnko4/ZI67X62NKk/90OmWcz1Inhokb1qCPpRW3AOki45ctCjxYes7HR7u4ddOVeq3R+N0f/D4A4LOHn+PPfvi/A3DslWpBYUUASGIPB6S0bGEQ0LmZaoiqodiXJTNeyqzFwISBj4ze16PjU0ym7jg7xxmyip4bwMyWtLT8HAJZYEBJgowD/Og9R519dxDhbM+NkbOTGVLj7s9u4WN26u5hZS3M83hpeR6k17JZBC0YKGokJBL3rW/9Dr72pe+7c1MrGO+6+eNf/Y//PZb7hI1Z7uPll64CAIbrSxC+myh7SYRPPnZto4P7DyCo9bLeCTC85ibc/guXUJCPWGA9xNTyvZQo7DV0+8BHSW0JXWro5xCQLMuaWyzG1PAIGzEcdIGBmzu10RiPGsNeiXc/dOf83if3kNOiuL62gS5JJeRZjpzG3tUb17C8QhumPMUjwifu7+5ga9Nd43iaY0xslqjThU/v/aQoUNM8XdQaV6kV30+6iHv9Ba8va9vSAJvxQrQeh0pVzJwztiWEW2HYT2oQA+mYPN9UglK6DXOZZSgp4am1wDlRuasqR5K08Ivmmeg4QSzIO1ADYuLukzUGBS2sWVFieThY6PoAoDSCZQkUWiV13wu4zS+EhKHlVUoJ0yiRCwFD5+8HCYKBm0vS7JwNUoUXozdoBGEF4o57bhoBJ6R6lkI2psdB0GJ1qoo3DVII+HN0eDxHIcBBRhqT1gpHB+4+e+UB8sg9l50qxMMpJXKdK0ioPbuZn2Jr5tpVoT5i9pwKI9T0tE+nBpLYtDoPEBd0TyYaOc1DO48e4pw2Yb3eGtaUG+/jza/DEOZHASyx0IkDRPHTx+lFS+siLuIiLuIiLuIifuPjqRWeLM0woTJuWhTIiU9/7/4u7t51Ja7RJMMRuRQ/fPQI17ZuAACuX70ETbLQ2WSKkLLRuiqREwCuLipIEo769P4e8sztDN9840VsrC/R5zOUlNVK60ORLhCkfaJE17C3ntdHqxIGq5coy57NuDT/8NEj/Omf/ikAQOsWCPV/v/02/uk//Y8AACcnJ7h79y7/fw1YTKnW4dZo84QQVBOe5/Hu5/XXX0Neub//4oc/wts/ew8A0O0luP/gMwDA9u4e/sW/+K/duT28j3NqySxS4dFaP6FFYWwrKw8qWwahz3YhxnqQJHKnlOSW07WNITrUx9qbjqF8so3o9dD0FzvJEnzamXeiABXZcFTpBEnH7bR0bVDSjrQqC3SaXaC0aHC8yjNcln5WWGP4XtZVjZzA0WuXNhlRakWrT6UksP258wcbnZ5icu7GYxSFXOHTWiMme/Jut4OIJOwrYVFQgezunU+xs+fYPcPlZdy4cQMAMOh3MVhyO0apOvjyV5y+yte+9R082HbP86//+q+fYBc+K3qxwjZVoiazCSIC9y4PO5BRI9xWIqVKzmx0xuX1Mp3g8Ng9h8O9Uxweu3L56aRCStdirWXjOmMlVzSTWOCr33Ytns3LK8jpnKtZicN9d5yllTWsXnY78KKoIKkN1JGC9VYWCSFE+w5J2YKo8xluXXd+dC+98jVY6Z7FdDKCoZbsy6/exk//3b9196rroRe7+WPvcIJd8oKbns8gaC7pS4mtNbcbjGIP8TKBh22JkpgeQZRwhcpOM4CAm/AAj8aJF0cQz6E1dHC4jw363brKsDp0u9YrcQ+f7bpd9CxLkZMcv4pjfnYq7KKfkF2PCrBG2l1VWWJGPkPDlSX0++7d/Xxnl8VTV9Y2sLzm5rnA91DuunE7ms4QJK6acHpy5sDKAFa6S+j33BjuRMnCRkxOH62xgWjnRGuM83wC0A8tLFUltbUMrgcMapprPE+h+dHJpISlNorRmm3X69pCE2BYKZ8tXKIkQk2VAWsFQCJ4QVbC90n7pTTwwsZGxjBwe5GoK4AuBbnOubotEg8hCUMq5cFvqvlo5zvf8zEhEgPqEkub7t3yZudcMV9KuvDpmGEYsmhhURpYUJXXWj6JPM/Z0inw2xY3rIWk9TCOoudqaenaQlLlryolTlL6rUzjmNbsbdPF/YpY2afAsOc+/zJ8BASoXvc9drSvRQWo1gOr8fmqrIImKT1bCoyJyfpoZx/v333o7k+yglsD97wura/Ap2dtqhJbl52Q4tbKJobPEMh8asIzGp1DU0tCeJKTk8PTCQpDg6u3iqEitdDjI+wRvbM/XEY3pn45NNMd67JEQf3JbpRAEjvCRF2cTNxNffeXO3iBJqa15QSywcNIj6lyUjyJ1ZlPdJ5nIYnjmDEzVrRGou+88w5/5rd/+7c5ofr5z3+Ojz92VLzXX3+NTULn1Z3n1ZLn1WOtfdKkrhmAFsDRkZvs7t+7hy1iZXzpy2/h4UO3QH788cf46CPXTlpZGTJlepGYP7eqqtg7BwKomhZSXaOiMq3n+5BEecyLEoIopN1Q4R/+4HsAgHd/eQ8elQ9fefV1nlivXLmC0zPH3jmfjBHQ9Q4GA/jkq5TlJU9sMAItS8sybR8KcxPh06Oua4jm3IXgNpaua6wQngcCyAhj0+33+J6YWmNGBn5Rp8tjYTKZYHnZLeKdJOY2mZQ+Pv7U9ac/+OgTzEp3nIEK0Bu60vPXv/V1vP6Gwy7t7R9zYnjjhdv4L/8rl7Q+3t7Fp5+2yfKzIjMeisJNlPt7x5zM+EqiN3TnXFQG41OXCO9v7yHLCBMyiLmldXYywUlKlNfcoIIbC65E7555HMf48lfdGLx+vY8r19x73O8n7CdVTyvYJcI7rfWxTO0S2QlhWDzSQJTP8S5SggkAVV5AU5tyuTfAl95wHnS93gbyzB0zTU+Qj9x78+ILlzCM/xEA4IP3f4H7HzhRz08/eQxN42vr8iaoW4Xs9BiVR8lb7KGk+6CzHAGdh+/7qGlxTbMZ8lPXPqsjBUlq67Vyi+2i4Sn/iRbYlOa5cyWwdcshDwIvYDFX+BGWTtwzHaUVyw6Mx+fo9RqD4g4vkGurQwjjxvnW2grG1L48qGs82nP05tXhCnxqyVUmQ9G0DZbXsLXlWoGm0lCy8fxawqIkJiUVggZDIgS3Oay13C7L/TUUDdt4zgdKQUAIdy+rokBRN6zBGjltSnzfR9glZm9dISQZFAgJTQnD+bRilfayKhCQ55sX+WzueZ5WuHLdmXIqnWE8frpg3XzMZlMW94uiGNJrZUcalqRRmhlS7LsGYp8287EVEMSCDjoCIOiG8EOIJjlRASQ92xqCN4Ge51FSCOiqRk0bWjnnQOBJxVASoRQrGC8SdV3DJ/yVKRRm0j3HI9sHcnf/Pz2a4pCwdao+Q9lz83536GEoCXPlBU7UFgAEWHrBg0VN6YeNepjQJvn+ySl2aS08GWeA78bpeQGclO58ZoczjGauyLK3s41rBPu4vLmBDolr/rP/7L/4tdd10dK6iIu4iIu4iIu4iN/4eGqFJwpDRvzndc3Z6Ouvv4blVZdBn08F8oIQ1jrFz991Dtp7+49AlSYEcQ9LBHysshkssUc6Sz0UVKYXMkTgu9LqrJzg/Y8d6OmFGyt46ZYr+8G27RVd1y2oVYhWunuuLL5IJEnCegynoxEa34WHDx/ilVdeBQC89aW3kFLJeHd3F7vkvfTmm2+wx88XhcW8/1fb1pKyBfEZbVhvZ5am+MY3nMfWa6+9hh557dy7d5/bZ6+//mqr/7NAVFXZ6rAAsAQWU56HjK69rDUCats4WwVy0S5KhLSDqdMxbl52rLCbN26iSxWNztIK+gNXZQjD9ru7997DmJgkeW0RUqk78CR7wvieREZjQBsLTWVUXWrI59B7bypwRggcHjpGyd7OLlY33DmGScwieGVd4ZR263mWcbuvrmtmfURRxLo6s3TSguX9CA/23e7i5GwCSywC60WoaXfaG64jJh2VZXi4c8+B+vuTIb757W8DAP7RP/4D/A//8l8ufH2jvMDREdkoHE9wcOrOc+8kxbUNqjJC4OjI3e/9gxEax4PNy8uNawh2j3LENKZefn0dIYGxdz4/4R31W2/dwje+6d65/moHhKXEchwhIDD73XvbCH33rK7evIzOqrvPp+Nz5OSWrpWAxoKlATj2WQMG9WAg6aSvX7qB9U0HAkdtIYltpEoDSxYex4dnkPQsrr9wG2eZqwgEcYSEqsvrK6vwCND52YP7OCdBtyj2mEUjPa9tsNQ1Sqq1j6sSY9KCUScjLG2Q4GjHR/kcjvCXNjfRI8DlaTHC3g4BczWwotzxfc8gy+kagxCCKkhlkXHbebi8hCxz57Z/cMA2HIdH+9had+Mhgcf36uBgH8dUWZfSR0V92VmhcZUEDG+//CoGVJH5+KMPkRLraXtvB93OYqBeATknxKghGiCu8BAFVJUWrXCj88xqrAoEJH23MgoicnNKN9Twc5oLpHL/AwBRtVVBoVistpprewVhzBo1ceJjMCDPv3GFy1fcAlXPRjg7PVno+gCgrnNufUPU4AK6J2CoAlPqAlo3zGFXNQfcgsvWD0o01mWQyodQbpx6YcQ2P5URCIgtpzyP2bBWgK9R+T63oy3AOnTVnCZdWT8f3ENrw1Xz47Mxtk/cvHIwmmKSuYrjyWTKYOmwynF45ro7SJcxXHXzSj/sYpnGb8fmiKi1Z2AwNsTarYETqpS/82AfNcEgzke5E7gFsL6xxl520/MRk5o6SYya7FbOTo5QZG2V+NfFUxOeaZ5iicrlnidwTh4z60s+1gnVnlUhdnfJf2UnQESlrHxaYkoiaytbG9xzm0xGCJOmjBejpGqwF8VQltRva4mSytafPx5hY8WxBVaHXabGKwj4jYFpVbFQoZLquR7s7du38Qd/8AcAgLffeQcZMciuX7uOkpKB1eVlTCjxy9IZLDFSJAQP5PmYx+rMJzsQv/L/zbG0QjZjLaDpxb28uYGdbYeVSuIIV7e26Nyu4datxbxtAJdQzZurojFjBZ5otzWGrdlsykrRRVFAkdqlD4Oycv8eBD4GTWW5nOH00C1yUkp0CAeVFyV2D11iMclK9DtuMIae5BZREoVISLk4VBq+1ySQdmH107qu0aw5VkocHbmEZ+fhfYDo9Um/j4TaEKPjI0xGzQSnYWniGI2nTNEdDJYQEbbhbDLGNHX/XtgU54QRCqMY1rhzL6oae5RoPdrewbWbNwAAXuDj3n0nXQBp8d3fcmP59/7B7+Gdd3+20PUBwN7hFHvH7hyC5RWMTxylc3KQcY//9GyGMS0Mg401vH7btaXqLMcv3nM4lv6VK/jO7zg8zMrmAAkJQ54cTaFp9r1yeYkp/MrzEdP9WQsV02Xz83P4fbfox0vLbDqpjEFI7U0rBIxY/F1MVIC6UVZVCmsbrqX44kuvQoWEe9EaJd3/89MxRpTgWSMQxG5eWU4ifDVxm4ZXx2NURLEPPQ/DFadgfPvVt3C0796t9GQbNm9MYBUnwNlsigm1RyeTKQSV5vtWYJkWuYm1qIPFW1pSlzg5cr91dHyK7rLD1Vib4/1HjlEVRB0kCb1D5zM2qz08PEQxcdf70osvsgegLQxG1JI5OS2QT9x3V6IumjzhZDRCSEKFZV3jeOTOIegtYbDinuPO/gFOKDs4n6UYURvp5GSMpaXFhAeTJOIkJw591DWJHfoJrlxx81cYhZzwuAugRbkoUFWERZIBtKYEyQKNqGtZVTggQdvA97FOjLTR+RgBjbt+EnPiYS3Y6BO2ZvHcpYFGRGP/dFSy3+EiYUyJhmBZVhlDKDzlMbNTQXCr0/dDVhCXUsKn5+lJsMmpNY6iDwA52na+lJKZi9aaOaPlYG4Ta6GadUWIOR8x9w4CgLKGpUAWiUrXyCmJfnS4iz1iNO7sH6LpUq9vbDAEALrGKcm73N/bBzJK8K508QopzYt6BEMbiFR4eFS653s8mqIg6EwdatiUpBHGOVP+Q1FhQmNfCclm0eh2WS1eCgnPe/pzvGhpXcRFXMRFXMRFXMRvfDy1wmNhucRflQYD8jlRnodSk4Dayef4dz/+XwAA7/zsQ4CqNGGYIPBcBtfpLMEQ6+N0kmGJjrNy+Sq6lOFKX8HSbqpKS9YW8WQHHWImKGVabw3IJ+wkmA0gnlN4UEq8/LKTm2/+CwDf/NrX8cd//MfumHUJSb+bTs7xuz9wLK3/+J/8E9ZCEMATbKwvqjL9unablBInJy6D/unf/h0m5y5Ttrpgh9gf/M538d/9t/8NAAfcex6ZcKkkbCPXLeZAwnpOy0hKLrVmsykLFXpSQsx/hnZOwtSYnLnz1CrCLGvZA825PXz0CDtU4RmnBZZJW2LYS/gzaZYjJuDhUifByjCmewLUekHrBWO5vOuHIY4OXKvi4FEP9cQB4ETUw9YLri1yvPMY1YwqPKJCRrvN00mKGZX6ZVbhgADARVEjJQBtfzBEl8Q4qzpH1YidJQFSKsV+8It38ebrbiwlnQ7Oj9x9+mia4htfcS7eN29ewZfeenWx6wNw794xNIGf129cQnHX7biv3drAN37gRCiPjkYQtMNZv7yEjcbp+4MH+Mk7rkX8na+8iK9/01V46tqw1cnacIkdi4VpHdKlBBQ929OjA5yPz+i6Imjy5DqfFjCZu595kcKExFCsayxuiAZ0/BBGkS+f6uPSqms59JfWoUUjgidcyR9Ad2mIdNaI+FXor7jrFZ4HUPvE1B6Wl8g7TKrWl2/5EtYuObDjwed3gJl7RmE5wvjI3as8S1GQ9liRZViids/tjUsIPWIF5hWmyBe+xmJ6ik+oNb22uoRlEg6djE5QSnctWQnUgRvPh0djFooMAw9x3+2o86pEh3bOui6wRt5bURQioSmmHM8wKxrSiQ/GjkofAVXcr9y4joT8Co5Pz7hFsX55C1Hsqnl1nbNo6LNCKUCCnqHno3n+YRRguOR+R0o5Z//TzpV15KMsSe9KtvCFeo74YaZjVFR537x8BQOy2JlOZiyc2h0MYKnKYeycW7qtYOh3r4gEcePDJRSi5OmtkPkQtoauuRfFQovCGG5FSc9vpiRn5UHflbBccTJ1C9g22qA2bhxNq4qhEkEQIKM1OE1TxMRGNRBoi1IWktizUgEevdNlUbLQn1IKnl3sGQIOItDABB4+foQRiecOl5Ywofmg3+vCp6qRCgP0qVI4ygvcO5/SzRI47rsTXUIESznBTIY4I9b3WJzjbM9Z+oizE9Slew/S2QQD0qbKx+dM8rl+4ybOTglkLgzr8KyuLj+zUvfUO+D7PpvM+YnHN15KicnU/f13f/0hHj9wPx7G61zG29xYx62brly7eamLWeE+Mzl8jBju33u+xSo9QG0yhB1C3JsBTO3+XQkDqxsflQw+JU5aa1RlWyZskp+6qhZlUHI0L1NRFNjZcWXlnZ0dfvn/5E/+BBmp4lpreTH4q7/6K/7MvIeJUuqJhIRf6LpF6NdVjZKSmaqqGEe0tLSE9993ooJHR4eccH7/+9/HT3/qPL+2trawuekWg0XYWlVVPeGl1fTYy6pCQWVL4fmsimqM5p6wkG1TLp9XbPZ8GPKjsl7NSsezdMbMiTTLkFVNLxooqSdshGqgUjCwmBA+Kg4DaKIxCx884T0rnqBiCvB9PTs5YlXbSQnsU1J5fPAYOT1PPwwwy90i3jAsADe+dndd4hQqIKJFdtDvc2IwmdYIiUXgexIxtVRmkzHufOKUftfX1rD7+Tadm8QeUaRHoxGODhdVrwUe7ozxre86Qce0Eqjovr75xnW8+eZ1AMB4us49/sD3eaYzLCAAACAASURBVGyurgx4ceyEAQvNCSOgSaKgyAqkDcPEGvg0RpSQ2H/kkkZZZAgpmRkXBmHgxkK2swurGyxYDZ/ug68kLzaLhOcHUOS51wt89ChpkTaAEFQWlxqNMkV/aYiCWE6T6Rizhs2pFFJa3Q/PznF1w7V44qjDNW0/6ULSMW+81sGy58bDZ2//Fc523DEHnQQzmmPUWGJl2SUb670+LC0qJ2WJU1KOXyQeP/oMHVpc1zYuYZmStDQrsbxMQn+DAc8fvXjQjlVfwSPPoW6ocGnNnY8yFWyjuSDaljIqi8nIJcZht4cBUXbX1y9hQHNSXmmWcXjzjbeY+eMJD6Z0n9nZ/Qx7ew8XvMKWJWS0gSH2W5LECBqclLDskWTocwAQeAoeQSLK2qLpU0sYNu8ttUSP2iiDlSG3slXgw6N5RwYeK1pIK+bm4hABSYysRh6EbKQ0+q1q/AJhqqpV+fcEq54bXSOb0SY8CPgzOqxb5pTnQddtItcs0Ma0m3YIQJOTQa0FyrTxjZKcYJRljmbUWasRkPxGlraivbqsuO35vJvkeVHUS5cusXejFQq/vPOpO77WeIE2keejETsKrK6vIAndWJ5OxzjqujEerK2gJpxuqSJ0KPF7YfUAnW2X8HgK2D1yYzYdz4DaJX75RKLXc89d1wYhvUOeL9nL7srVLWYWflFctLQu4iIu4iIu4iIu4jc+nlrhqaoSMZVxkzhBmhKzJc9xsEtOw+UGvvaWA/0W0sAKt1vYutTB195wOgeeVPi//k/n5hvoFAEBSY8+v4+rW27XsdIPEBDro64tBAFZta5REJBYeB5qynxN3XqDzPtnOc+QxUt38yGl5Ez25s2b+KM/+iMAwOnZGVd1giB4oi3FzrRSztlGiF/PxvoVTZ75llxz/i+88AJn13VdM3tkaWmp1YXpdJ5LRMqauqn2QkpwZcxq2+5sqrqt9oj2uowxDIKr6xq81TKauxXKGiQkuGVKoBuTq3cSQVJbKPIkhgQa7sURhKn5Mx4dpxuFDNwz1vDO6VkhlXriniUkxJeVFaapq07sn6UYf+LAw+vLPbaH2Ds+Y9CvLwGPAHBx6PM5BmGIhBheeTpjkcPQ8xGErVCh1U2pusJ777zrvhsE2CdWX9Lp4i/+/M8BANvbO9ilauIikSz3cP0lB0L+2Y/vc5t3eZhgNnUVgDSrmGU4SSs+HwiNLhEFRqOChc+mkxSqLbVxexZCQlOVTFcaoPK6jgJoarUknR63BCwsDOl0xEJA0o7dGsMVwUViVhXwyCW663kQVaPZEUIS48VYi5oqhVprdGm3H/e7mJFO0cnpKR4R2F94QGfJVTw6SYe96aySrAujEGP3gdPtOR+dod+jyhIsJqRlFHdj+FSV8NScQKJ5PjCoVBJDYlgeHp1gRiDwMs0REksyDEOcj1zVMfQ9ROQhZK1BQhY0yuSwdK9CYTGlZyqDqK2G1AYpvevd5RWsrrtdt+eH6FOrIEx6zITxhOK5M/RDBMTgy/Il7B8uVm2tasMVUGNrSLLm8H2/1Z8Rou1oOVoRfd5w60coyVVmpRRKAhunhUF/uEzXobj6rHwPYVNZjAIYqhSa2nW8XRhmolaVI6AAgPBCxL3FrSVsVc7N6RY1XYyrqLp/r4qM72WtFTzyXisrzWuJLwXb+WhdsfNDFMXMOFNKMDspCkKenwDJ9zPLS6T0+dqr4TV2Ep7iz5Rl+Vz2S0IIXmNee+01bm99cudTvuedbhfvvfcenXOEXs/NB9XxNjZIUFGtDSGk+90jrZihbW0BYRtNIR+Xr16l67WwpI81nU5gaQ7WdYWG/3B6eooNslmaTQ0m5+5dOTg8fKbW0FMzg7KsMCVV5KKouEReFAXWSC20113CwTn59ExyeFSie+nGJlaW3YuaZjW6667sfvura7DksXVSZSj23YJ4DT1s9CmBqQo2OwQEBHl6QCvUzUQ8J+A0L+inlEJVLYj9+JXwfR8bG24hbP77mxBGV8xok0ryAgkoSHoR67piNVAv8HiwGwvU7G8FcJZjDSy9rKYuWTQrDgREowaqSwQ0hV1aG2JjmZRtA4+ZX3HgI6AFI/QkGsuZaVmjLhb3mmpeZiEEQirjvvDqq/j80UMAwKMP72FMeI/l/ivQ1M++92AbBSl7Rp6ET8cZDrpYHbrzlbCsFipgUFfu+nxfIWhYAb7PYmemNjglVkNR5IjomN0kwSe/dK2u0fk5+sSaWSRu3FpDt+8m9M0rPQwekU8dLE5JBsBAISIKq7QVNNEphAT7iE0nE2Qz9/zzrGgxcRowxIwMkoBxW9L3sUJqwHWtoRuMQiDdlwBIqeBRq9nzPZTUhh1lOWMIFomT6SkGlFxbmaAcEi4vjqAYh2G4Ll1Jhx0AAFNYJJGbcI+KA4CoqlcuX8YSPccoSiDpPdAQCAkzcfr4DvY/c21kz6ZQito9RQlukM8pjhspENCi7peaE7NFIogi6Eb00hicnbrJOg49eA1bsRfDL9y8W+YZi0wCFsa453t+fIJa0calKhhi0Bmu4uDA4ZFm0xRbpP5dhBFLgPhGYY02speuXMXZGdHVrducAi5Z/XzbeQlOZ8fok5TBs6IoDQw9Q8fWIop0VbUGvHPSIVVVMZ7HzAm/Ctlie0QNZCkZfZYVVpbJZ6o5aQDaGJimHQ7LGzMh+SMAgIAUyvMi46TIiyLo6jkwkVrzJklJxUUBPcei8gIfAbXBhXwS4qBNA6HIWSlaSskb0aqqmG1Uljl7NM5sa1wdBEGbzFQls7GqOcZqHIQ8P0VR9FxyLdaCYRZZlrFsyvHJCav8X9nawvXrbl03xuDzxw8BAOeHh7g/bYx2IzYANRCIaIcdKwsQDR9+BEuYOCsARW2vWy++iiwjeMLZOY5O3bh+YXMDFfUsd/Z2sU5tYSsEP5cviouW1kVcxEVcxEVcxEX8xsfTWVpWoCiarNzwLtr3Q1hqSy2veVgmT5frhYYlvYRhrwdBu7LSSPRWHMhWRJol77WpUBPgb+8kRTdwuwjPWlR1W71pqhNFXTIgVQjRAoDrisGRnm0d1ReNJvv+VeuHX8e0+iIG1hcBwuZZS18Uv/qZRY7/PAC0ojaAagCAFoVuS6HzJJqmiiGN4XsbhmGr4SMkoka8ryx5Jx/HAh65bhdVDY/Apv1OgutXSPJ+ZYgO6V7UdQVJ5V4BC02sjtoISAIVpmnKAOZnXl9RMHhbQGCd2Dff+u2/h3UCd//k/TtYIW2Tg6NT7B8TiH5WMBCw34nQIcZKt5sgIS2aqizYt8bzPdbIUAAzNGABn3Z6QrQu8J70sETl8iiK2aF5eWnpSY2mZ8Tm5SWurmxsDbCxRt5PWcV2K1WZI2yEyeq2dO75Hnyv0bcxmOUtWL7RyamyGmXVtKIqdOhdTwIfKYFmlVSQPAd4yAkwXJUlBFVLJARbS2hYiAW1lABXTQoJgahnBjJyz0skHQbQmrpilosQ4B21NdaxZAC8cO0GtlYda6mu2rFWGcttKaUUbOp2oeePP4E+d2KSAUpUVL6fpRmyjLSpiprBoOdJDksVrXySY1YuztK6ev0mhtSankzG+IzsRcq6QpG6a+nkJX/m3t07aNokW1tXEdEcKYwBpk7ozeQps2CPU826fLUU6NAY9oRA2VQE6hodclrv93qwaLSkKhxSm/XocA8ptdtefPEGynQxYHZZlG2VRghuhcxmM6Sk7YW5ln9dt4De+XnPGMPj2lrDFSxtKtYCE0LzXJ/nOWv7JJ2Aq/x1rbm1ZG0LKZBCsrleWRvMpos/Q6trrqh4YcQtvKqo0EyowlqudHlKoiqb89c8r5VZyt9VSnElp65TBLTm+UHAn0mimJlfVZW3ZJuyhEdtZ08qWJrfDVrYhJTyudYMz2v1qNy5uR/udDpYJbuehw8f4vJlN9dmWYbx2I2Rza3bzOgOwgCe30I9FFV4fE/B85sKTwhB+mtCelyFEWi14Q4O9rC94wgfR6fHGM3cb21dvYob11yrv5NE8P2nCwE/A+wi2ETQmDYZiMKYWyECgplTcTfgMliVnfNRlASuXWqQ7AEkvWBCAhbuxR6fHwNE8TWVgGX8huXyVVWVbTJjwD1ewLVqACorPgcz5ImrXTCpeJ6Bs8hn/0OSmcVOQnIPXAjNff1JmnOb0vM8eFQLDdG2iOI4ZrE5z/ehqERqy4qZFlYIVFxeNfBp4YzCkEW2Yl/CR8PYqkEseQirmRFUWWZ1YpZnsPViz3H+ZQ6CADdfdMyBbq+HM2IOrG9u4jvf/i4A4OP3f4GPH/y1O6/OEpa67lp7YcCeYHEco7EzshJIGrNI34duEkCtmQIaRiFPLsZoeM2EpTxeQKENC0xqrXkxWCTCOISh1zXsJLhyxS3o03EKP3BJXZEVmJE4qLEaMzq+hORSr5AKad0kmJr74qXR6Aw6dG4WdfPO1RVMw8D1fdQ0YVVFygrZeVkiaUxaawuPkuJOEjIWaJEQwkeZk7GiiJAk1FKUASy1tauyQK3dPGGN5fK97wnUZZOw1e3vCsm+cFJ5UE1rUueYnbkk5/xwHzNiM2mlmfY8mxWoiFGTZQWm1FbpBFNMqQVyPJ4hrVp237NCeh5OSdW3yFKU9N08KzBYJjzjcA2Hu461kk5TrG2457t+6RrSmfv8tDpEl9oAa5uXYMiHMAuX0WiyVVUK1SSuYYJwuTEJPcf9+84PLur0cXDi2mqnp6c4JkmHyfkZlqkVWBU5e7E9K6q6QFE1yukaVYO9STMWM8V8e0fPYTGNZsyXhmX2lq0rvm5TWxRFozxdMqazzFPUpAI+8luMkLCAFnQ+1vCmAVayj+DpWYl0wYTOXWPNHlXpbIqUFl+LlsGrlIIfENYv6jxhotqsW1JKngOEqLldFYURs42iMOTkJ/D9OdyqZHwnhGQcWTfptGw4SKhmjraWsYeLRBh1QB05JJ0+eiSHsLy6ictXXRtrf3+fVfWXlpbw+utvAABWljcQE1bKU4BkVVgNww7OPjMClZDM2lXKcxRduqMNrLCqC5wTVmeaZswG73Zi+LT2C2Gfid+9aGldxEVcxEVcxEVcxG98PDUdmt85F0WBKcnueypASOmflJKzvF8V/GvsA6woEVjSEpgTo4rCEIpQ50prjMgfqKwrZhp4no+CSvB5XnGmrLVuM0SvzdvmxfEuwkVeFFwu1Vqztk+n22FkfVEUDnwMV2FqdhtCCPgNMFcodk02tgUH+6HPOzNjgWmzG9O2dTy3FkUjoJXnXH3wlUSPHG5ra6DJ9iLNc0T+YqyCOI7ZLTqOY261fPzJJ3j7Xce++dJXvoIf/O7vAgBeu/0SRrkbM+/94j2srrjya8cDC375ns/sNBWFfJ+stchofOV52bL3fB+zZmdbt4KOEIJL87Boy9llibJaHJQtpQUaVl9VYXnNVWM+u7uL3W3X3vKVQrOHUUqiec8sBDzaEenKOuYVXItK0LlJpdjV2cCCiDswMO1O0hPIiQ2kLBD77U7M8xrPnrIFPMOCRTIWCFFX8KhdVc2mqNJGiHQKmVMb0Rr2H1KeYvuXuqp4nFZFyWO51M6rB4BzFycfuXR0hLsfOCbd9qPPIOjzo7qE1u1YnpFGzcnZOWZk8W0xglLu3I5HZ8/lpXXv3j0mXlza3OAdqVI1vv61bwAANjfWsPfYscwCP0Zjrj4epwxmVV6AJHG7boEcJVk4pDrFYMkRLobDAc7O3K5YyrYFaQCcT1xF66Nf/hLHJOK2urrM7d1eJ2IbgDuffMjV3GeFNhUMvfOe74OmfUzGU8zIaiiMAp6PYC2/Z9YabkUZJbnCY8qC4RH9TsyWM9YaWBInLbIpO4yLcQuKDvwWgF9rDauad8LAaLKWODpBXjVEjmeHF4TtOwEwDawuy9bTyljUZJdgIeGpFtSvVOPzlXD1RhuDgEpzkd8CjEO//S2lFK/B81APazRsUxnzA25dhkHI/nJ1XT9XRVmokM/BlxIRVVu7Swbrlxzb79VXX+Wq9jwsQ1rJ7Xoh2oKeFIBup8KW4SwEmrat+15rk8GVOiQYkuCrhmU2ltY1jxPAsE3JF8UzMDyWW0jWWi6n+b7Pyc18UiSlfAIJ3rQHPBU3lhhuUDfUtNqgJoxQgBhRQNiCsuJuVZEXTN2ztmUYGWuQJI2oUsAvgblIdv69SIsKQUBMCGN5Qu93I3SI5loVGZr+hmtdESYnL3gMWSHYE6Y0Gj61GXSeoyyptFxoVu8ty5LFvcLQcDmx1sB40igUxwgbopMUqG2zqFj44YI0Sm2gaMFNghCPyazzeG8Hmphe1y9fQkSDsBNH2CKfps8ij8em53vw55Q6Y6Kcl1WNnBhYZVFy+9TzPG6fno/HnOTM0hSiYXEoj//WFsjn2h9RvHiJOZ/mmDTMFl0jiKi0rS0eP3CL2pUbQ8bA1LXm2UV5AkmXaPW1hqWkS3mypZhGERIqQyfWcIlZVxUbvUJr+LRQ+dKDoZZjIH0ENDfUslVX1pVxxoYLRiI0YuHOLTs7xme/dMypYO0F6BndK98iomuRykNBCVhdlvAIowVrmKUjlQ9N51yUJZoV+OxwFydHDq9SVDnqqm3PNSyqLE1ZcDQtKhBhBPU45RaxE9pcPKlLkgRJ7Mbe5uY6L+RaGwx6bkJ//OARyqIR7OtB0KKVdAfc87VSsfmSkgJ+RAKS0xp7xNLq92M2Y620cRx9AEEYQdJzOTo5ZkZsXRXcXo57CdKZawfX2mB9/fJC12dMi/U0eoa8ILwNfJwTxmOoepCySXI0J1NSKhbrQ6UZH6c9hS6pjGtTIyPmsJCqVTOWgltpNk0Z01dU5ZMbcfrTWoOSmHxJF4jl07Ef8+EHAZtVG2OYRaeUD+E3C71A1TALUaCoibmqAk5+bCVb02sLZBOHKau8Ar2+w/3VdcXnKYTkNTUKY5576qrdxCopWXrB8zz4UasgXenFN1jGon0WtjU8lVJxwjYfrp3XYEDbv919aVpOv16ixcm4/Ppm0xPwDvqMheW1SsCDEXNs7WeI1V60tC7iIi7iIi7iIi7iNz6e3tISAopS6NrWnIlrYxhUNS9ZXZYVZ2q+77UgTggWNdMm5zJVbQzKstmFRlhdI0bPxgrGY1e62987YeCb54VQymVznagVYTLa8PGlkFyFWDSex139/2+xCMA58APWEBFCsoiesgaWSsi+tAzuDDyFRptdirbKZ4xFkbb2IkXWlMg1mtzZzPnJjGdTxNSumlUVH7M0hv174jiC3zC/8qx1x01iqGCxCki/20Wv73bHQRBgRjoRRTpDjxzPT3d38eG7roXx8Z272H3kqkDrwy4Dkv1APjEWGuCu7wdP7EDadqvgsrKxhndZEIKd1qMo4h2m1hqC3gk/8J8LRCilh7phLgoBSZWoS1dXmRnZi3yHsAYglIKg3aCSAi++6MTupvn/w96b9UqWXWdi3977TDFH3PnmPFZmFatYLA4ii6QoyRYtd1OtwS0ZMNpoP9iwAfulHwQD7j8guOEGGnpoGLAN2C3Dsi23IWiAREpqiaJUxRJJVRVryjlv3nvzznFjPnHGvf2w1tkR2V3KjET7wU6c9VKRUXHP2fNea31rfcugzp5RncQ288jzfIhinwljIcooTiCL36cJUs5aSlSOnMfHcxRMYfW5niVi81xlieQWEaEBn48k4wW4/8GPqc15BRtnb9KPKgFWmCOrXqthzPXLwtEIOiOIx2SpraXkeK6tiu26LnL21HVWVrC6RsHAJzu3kbDXK4fCmCH0YZhYMrjYeCgAgXGYYMprX+oc9WBx74AQwiYBPNqaol4nr5SUOe5zxtZ4NLCZaJVaFUGdrP04zTBhfpPecAzDMF9lpYYaZwJeWVvC8TEFRfd6Yywt0/fScbGyvMZtcHDEla0TbVCx6KtBwuSNtUaAjM/XPM4RJ4sFZsdJDM1Bp9Jx4LLHw3M8TDn0odms2qDT+Swq8gTMvMMFr5VSygaqh9MQhmFvqWZlGuq1KgqmIOl5dt1FSWzJOI0xyAsuICVRYe92o1EFnmOlaq0tpGIAJEW24jS2d5vjzKArkRsLe035bAIAIx0EXAaEYKuCn2eKrFeEF8zOnUq1gmqlKCWk57zRc2dWniPgRkRRZIP3gyB4LrLaefJcx3FmKI4SsE3617KLZ0HmmW2RELDvVUp9KnceMEdE+SntsM8vOMAAC5vOPyfPc+TPSHR5RgyPsKl1MMaSUrlK2ayPPEs/lc0xNbM4BukahFzTKElDOLwJlHQQVGeEcQVREyCAGkepX6zYQzyOtIW3ur0u+sxG6rrKAoVKCXuILyr/r2dF/X9MDk4niAq2aiHgFkVC4yEcvhTzLLcL2XGiGTarjWU8ThNSagECLYqMhwy5reGk9ayWzkmvj2pCFz96IwufVPzZBRFnGr2CKTic2AB9ykJYbF7qjcYTsGqbWWTDcGKhiru3b+P99wgiOR0MbazO5saGzR7RJrP99n3fZhwKR9kNWanMapcJIWaXvudZOFUbPIHxW1JMx4GOZzXZnqfIrefCFgh0FWU2AMDZMx1I3qOedOz+EErMDI48R2eZ5qGRacvAbARQpEHESYqI40BcRyEp3PG5hoiKAr8OXH/mgvcYkqu4jsXmFQRSfv4wnMxqPC0go9EUS05Be2BwvEsM1UeDv8SVf0jFWGXdt8SASRwijulc6fe7ECgK2xpkPHduEKPKfYmVY2N78iTDhOH0aJpYV3iWG0ubkaQSEacThqlByNmH02lsYyZqroLvLq64VioVTEPN/R1hwhk+cRShVSflTeeJXZNrm2ewvEGKSpzPSOuCah1JQn+bCwceM50H7SVkHICVd3Obput5gYWufM+1F0a1VrNndhaHGA8pnmepsYalNkFv/dEE4YJp24Ffs9DDhQstrLEykGfa0gO43qzuoJQCT7LV0+cg8G3MiTHGFpEUQlqYPAgCG9/peZ6tOVapN5DwGuz3epbgM83SWTHsLMNpn8ZbSGn3zSKi5kI3lFIW7lGOM4tB4r4BQBanc3FHs7AL6RjEPP/a9+EUTMjCIGPFTDnOLG5LCUSskA5Hg5lCohwoMZdt68zS8CPOfI7T2J55i8iTFAE55uMBiwy1WeSN/Sv+vbFjQv+eKTmfdtcSXcDs359G0WKMsc/Rwtj1O19v0ehnZ2iXkFYppZRSSimllPLCi/j/M5xTSimllFJKKaWUsoiUHp5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXngpFZ5SSimllFJKKeWFl1LhKaWUUkoppZRSXnhxnvY//5t/9Oum+CyFgpQKAKCFQC7of2md299LKaEN6VCZdpHoFAAQJiGMEQAA36kikMVvMsTcBKEUqg490xd69hyjoLkVSmi4hp5pFJCzvmYg7fONEdCgz7/xT/9L8awB6GoYL6JnClcA/K5AKiiV0PcagPIBAInWcORMT5zXGDX/NzcaWtO/fvzJW3jnb94GAOwfjPCZV78MAPj3fu7v4MPbHwMAfut//1/x0cc/BgAMBn0Me0MAwGQcw/c9AECz5QKC2pnGKbI4AwBsvbv1zD4++O1/ahJBc/f2jz7CP/vnvwmAuvqtn/0KAOBXf+Gb8Fx6VKVWR63WAAA4joM0pffWajX0+30AQL3VhOPQ3IVhCN/37e+zTPNnF0LSM4UQKBqqcw2hXGqDUJAufRaQ0Dn1y1UA+LP3ub/71D7+3v/4zw0MvVMCcB3qa5znyCW1SzlVaF5rGhpG0Lp1oSB4XefQcLgf1UYNw96InqkFhEPzcP/xAL//Z58AADJI/Ac/dwkAsLbkI+P2GpNBKl4ZWiGO6V2TcALF71paWkJQoWf+4i/98jPn8Fvf+vt2L7qua8dezq1FIYT9t1IK2vDeNMLuD0DC8KrVwoEU9H3guKgG1PfpeIxqjT4bJTCOIgCA47ooJlEqQBrer8pFrVIFAOwfHiKME26bsvvgt/+P/+mZffw73/pFU6yXOI7hOLQupmGIyXBk+5jnszPHcWkcglYbRtJ4jntTZNyGOBqh4lN/P3PjBhqNJv0mjDAchwCAzTNn8NKV8wCA7uEusiyz47y5uQkAaDabyHgfVGs19Ee8R6MQQtGc/tqv/doz+/hf/5NfMCYLAABRJHB8SPup3W7gC198FQDg+Q729rsAgKOTY5xZpjYr2UCsTgAAYbwFh89LL0hwvE9zNDpWuHjxMgBAug1MM2pSvRLjbGNAv1dVDBJq8zBTaGj6jY4G8Bs077LRQm8a8ntzNOs0hv/Jv/+bT+3jP/qv/gtzsU7jtBlk0BOat2Hi4K07j6ntTgCd029+6pU1vHG2AwDIcoEEs32seFwD30fGc94djBCl1O9wMkXV532QZegPaU5irRHxujsdhXB5L55dW0GS0HuHUYLTEfVPSgfLTTrv/vH/8IfPnMOjk33jCnqmI6TdE0YKu5/SNEOW8V1lzNy+dCB5jTsCMNxObQwE/61Syu5vAHa9pzpHwmdMnhtkmbGfU16zUilAFl0QcPncz/LZ3750+fIz+wjAzO8z+6WxxxC01nP3v0Hx8yTOZ7qC1nYcMHcHGGPss7TWto9xliHj52SpRprQ+OSA1ScEhF0bSgooafj72Vh97tWrn9rH0sNTSimllFJKKaW88PJUD4/WCaz6KnJrJWpIGMGatdFWU8sF/T8AMHmKKBkDAPZ7+zAgjWytuQnHIysCUiEx9H2UpJhk9PtApIAkKyg1DiRr0zVPolalz2GWYcrasYCEJkqjmgAAIABJREFUlNQeYwwMZlros2SUjiHzuOgwqkEdAHAyPEG/uw8AqKoKAo+sLOH76HbJ+oriCDFbvyvL66jVyMqt1QOkGX3/13/zA3z0CXlyDKq4c/8eAGDjw4/wZ3/+JwCAh/fvYzQg6yscj2FYW6+4PqaTCQBgqA2UxxpxbqDTwp/0bImiFL2Qnn/a7eKbP/MNAMDd+w/Q755SvwzgsUWdp6m1ZqUQUGydJHFsNWud55AeWdT1eh1JknAfyRMEkFVRWMtKSmuF6DSFF9BacgMPYMsmyxJotgayDBBYrI9CGHTYQqtVXIRDspp7h13I6jL1Q/owvJalAoTi90tlvS5pZhCPySKNRkPkbBE5jkQypd9PowiVGvVbeHU4fovGDBJacP9MipybLgyQF1aclgiCCgCgWmui024u1D/qo7AWoNbaet2EmFk7894epaTtlxASgi09QFqPqRYakse44ks0Kry+wghLNWrbJM8QJvyXQiBn8yvRKQR7UarVBioe7b80iZHxb4zJ7PwvIjevX4fDaypMIkx5TUkD5BF91nlurbgsy6D57IkhMOaGxl6OopNZquCzx+/CxjK+8pU3AQC1ZgfjKe3RVquDdp3mxeSJ9UrNe8yklMi5L0ma4pM7twEA4yy359MikiQaIa+xYT/CeEzzWKn6OO3RXlxb3cDRIe3Xbu8UK00ak9OTEXQQcxumuHx5gz7nB1Ds7VnfrGM0PgIA7Oxtodam9f/S5RUkEe3v/miKEPRMv9NEvUp/21hZwojPwkfdfQwm9P2o38e1q6sL9c9TLqou/V09cDBiL9EgjLFyhjxPJ9MUe/s7AIDb+0Nc21wHAHQ8wGFrXWfaekuUlEhimqtm1YeKeP6jKQKHxl4AqAXUv0A4qPN9kGQaiu+DqufaCy9NM9TdAl1wUQu8hfoHAJNpiHpAZ73vubDTbzQKiEC5CjmfMXme2zPUdV2owntjzBNrubhH5/e6lHK2r3MBzZ7sPM+R8+cMBil/FpmxqICSCoL3gTAa8jnuxaIdn/bdp7Uzzw1SvpM8L0AYhrZfRXuMEXNer9z26wnPtJS2jxDGjq0rJVzWCebPPMBAipmnaN4D9WnyVIVHiZkrTuusuJcgjYss4YtMzC4lI12AD1ZX5WhV6fPh8RinPdrAFW1QXSY3setUIAsXXRYjnJDCo30Foei5oziyh+Zaw8NLF8j1HKY5Hh6Q4pHpHJ7Hl7WBPQQXkf/79/9n5MmU+mIMVlfpELl3/x4+ep9gprOrV7C5Rps1TTUePXoEADg+PrZwyOVLN1Csy3rThcv7Z/fxfZyeRjw+wDSlvx38/u/j+OAYABBPNBzQgduue4gVKTnRNIcqICEZQYMO9Ok0JO1yQTEaSEMa2zduXMbXv0iH17/4P4eIp+QGzpMUTo0W1GQaIfCoPVooGF6AcZYRrAEgiRMkfOk6joPplMYwqFSQJsXFP3NbKqXsQR+4LnyGT6QA0owvszSDYBgmzWcXZeUZ/Vtu1XDtygX6re/icI8mYvfgAEYzLOYqCIY8jDQAQ1rKAIbflSYRwpDGPskyKIf6Xfd862KuNyS+9OVX6F3Ns2i3GXaZ9OHxpe87Eob7IUyKNKO17/gOjk/p84NH76PdJMXw69/42jN6CCRJMnf5CihF71LODDaadxMLKeCoQuGZV4YkVHGKKA0JaudypwGR0mW30qqixYcUcolBRHMrIMiFD3LlN+oMteQG3S5BLZMohCmgQ22s0riINBptxIX726TI8/l9PDsEHYYolHDsXs9NBs+n/nqeb5WQLJ8gBbVhe+8IN8e0DzbPncf62hr3SyKNqY9JFCJl2MMA9tJ1nBmM6CiFpQ7BMIPREJn+N13/f5uMB4CjaGxdL0VAWw5r60sYDElROTw8wp3bezQmHYn+gL4fjgymQ5qjZrMCV9H4T5NjBHwBjwfARx9sAQAqQQfV2oBHL0A/ob6EE4HdQzLmgk4P3lVSOBJj0GXocDDN0D2lfXl6MkKLDYpniYSA5PHwlAflUQd3jg6QVrnfUiLnPX/3ZIx3d3sAgK9f7iBgWD0TEgUAohwB32fYWxhrQIRSw3MKBUMgZOhYOhU4it6rpAe+GuD7ATTDd66ToFZhw9tx4fHzF+qjkPZ8SuHZEA1hYO8qRzlw+TzQubRGuJKwZzog7X590lhRTyjaxW88ONBsScXQSAoDUgroAkHPcyhWohQERHEeQC9sQBYyr9jMf1d8b4wBTwWMEfD40rt96y7+4A/+EADw5ptfxhuff4PbIODzvZJlEfK5fTM7twDF7/Og4LBC6zoeHGcGk2k9C6nJ9OyMkfLp92IJaZVSSimllFJKKS+8PNXDkyYxfHb1ea6EcotgY8cG/FUcAVFAHtAYsOsROsKVi0sAgPVeFXc/fBcA0D06wvTSywDIlZyzpmakgldAXX4VY3ZbdiehtWAbVQnXo3e9dH6DLHUAO3tHSBJqzzQ11uuyiGzvfDDTuLMUuwe36GMm0F5bAQAEnTNobl4DABwf7OHSzc9yO+/DY6uvvbyJCXtRhuMhhCIr8caNzyKckJUFR6HdIRdzJWgiunQJAPBwo4Nul6y4PEvhsCs0S7W1ErYe3cHOHnmHhtkQOlvcizUeT7C+THOhdI7+gCCfaDLG2hp5e6rVirXGjdYYMsTme751H84HjGpHIYrJ2tzf28OEPSO1Wh0VDmBtt9vwOAhYKYUpW9eV5Q4Ma/ej3ikctgy01kgTWj9xHCNiGKP1jP5dPreKVp0sum63izF7Cl3XQTYXBFkENhutYdizkee59RJEaYzhlD08eYZqldp+9dx5uD57v+4dYql1jsbDW0XC74oSB16FLG438FAswX7vCMd96lOj0UAK+twb93B8evCMns1ESvWpbm4554EDZq5hGNh9A0hotoIEJEQB/+bKemxG/Rg6or4v1avwCld1GtvA4GicwGcPbq3qosqddFwXJ7x+wyS1FprO9XNBWg+3H1lLeDqd2rXWqNfgFwH1lQBu8V4hUZiY/WyCJKX253lm++4EAhvr5FFWjSVMuDnDcIKMIbPJYIpx75jHLYHLa9b1XEi2KqMowWmX9k1uDPwGeeeU6yBO4oX7OB7k+NKXXwMA9PpH2HpIe7pWd3B8Qp6Ok6MQ1WoBfSprlkpXwjHUtjTOcHRI3pgUKUZDOv/u3dNwXPKCX71yBUbv0nvHffQGNJ5tdw25Jo/GZJxh75jaX/EEIg5mjhMfkwl5vTY2N1GrP2sXchulC5/P8YovkfA4QXrY2z0EABhHQxVWuajjrz68DwDw9Rl85WXaWxVXAuwJU76ANAU0bpCk1I96zYHHa1lBo8FJACO4ePsWBUjf2z/CzYvkybt83rXB0n7iQ3OIRmZgobFFJPB9TPnsM2kE16GzwZcCwpl5VYt7RYoZmCSlhOB/6bk9qrWGy95zx3VtcK+QEjOfxcxrK6Ww0SaZ0ciKM2AuYQbS2IBtA2GD6/9txBhjIWshZoHlx0dH+N5f/BUA4M/+/HsIJ3TO3bhxA598fAcAcObMGWxtPQAA3Ll7C1/84ucBABcvXrR73XUduHx+GE/CcPKSo1zrnUnTFJqRpUwIG0ZD7Xt6H5+q8AwGfawHfOlXAiTs9o21QJcvhtV2jXx5AI7HIzzu0290FmOTo+/Pb56loAwAe709iArFySxNTuHzQl5Z3QB8cq2ejCNM+LIbpxlcHtQkz7B/SK7e1aaL65ttAEAeDtGbsCs8zzF9jkM2qAhU1BkaDFlBUKN3hWONEcMGjncNmX8dANC+fAX9ISkDF1+9hArovUmqcOF8g/s+RhLxpatOsL5GY+J6OaSgQ00gguSYievXAty4cYmbr6A5y0lIYd3rp6f3MOXMEGQaCosv3uPDfdQqBPnUmk3kY9qsg2mMK0s0hqlIEE7o0KzVOzZGKE0SgOMqXN9HyNDV/uGx/Xza7dqMmmSaQ6zyBRnFGI/oUNZpgrPnzlK/PB+9AfVl0O2jViUFqVKpwGU3c61ex8npzkL9a1YcxJwNcnx0iMNjgleiJIWSjHlnqY3bgRT2MFJKIi/cykridERzu32wh0sXrgAAprGDkDP5PKcGh+Mf8szYTKigXkXIylpqUuuK/fjOfRwd0UF//fp1OFUa74vXl2ys0SJijLCZDxSvVmDnpPQAFKtTbHgBZY2JNM+tsielA8FwnpQCBQ7bHU1n7vJsipUa7dFOrYoRx6n1TyNcXKE9ev1CG4eH1K+pVuiPJb+rbcdT6wwQTz1inpBq1djLUi1Xrcvb9wIovvwcqaxCJbWxB+5abROuQwpkOt63B2hvYNCo0qW7uroCzefN7uNtKN5n7/71e4g5fub6lYs4f+USAKCytgLNa2aaxtjZJeXh8d4eNi+SUtHotKCcxffi5mrHxsolqYHLRuTO1j0UsN3q0jIgaE9oRyOX9HtZBUxEe9dRGUZDPo8zF55H7VnqJKhXSDnJshCaFfsoEhAZPcdtVHGBjdEsN5gy1DzWp2D9AXHsI2CIcPNsFa3GgvMopDWAhRtg74QhuPULWM45w2x8hJU1MvwOJjGGU/rNW59soVWl93zp0lk4fK84SiPjMXYcCT+htVD1DRzGcozOUWFF9ZPdHt4/oPM3Vi1sdenzT8Kgwopz5Dr24s7i+DlOU4pTLeA2xwgUKokUeFKpKKAgIaySAyFgEyYFxYECBFk3nQI2NLDBKwKY05Yg+XJ3pILH70rTxBo9nqPQYGU5cBw4is+2xMwpTotJYWAJIaxRmOUaitdvmiZ45/tvAQD+9Dt/ag3dX/2VX8bBHhlAf/ndt23sJIzB/fuk3I6nA1y+TGEi165dtUqaVHIuBleg0PalMLPfSGMHRRkAFuoSNkP3b5MS0iqllFJKKaWUUl54earaXnGBTouso3qjgT3WmjOdIWXra5prDEOyNHZOQ/Ri1u51hu+//wEAQI4PEDN3wkpnGZ0maaBrrToqDEu5gYfDEXmNulMJw9q9C4MqW2WdoA6H3Ycf3ntkXc+TaArfIS2y4cQ2aGsREVIhisnlPRq1UOfA3Ts/fh/f/x5lUUEJfOmnvwUAuPb6N/HJPXKXyjxGVXIKizBotuhvz222UOMAvaMB4EU0Jp1mApFToDXSHCJnq9ikkF6dn1NDnjOsMwkx4Wj3eivA5lnytqnDHpJk8T426jWAvVWxNpiwB2wwGiFid7xyHTQbBG8liUDAXh0YykwCgIPdxzg85ADK4cRmHGWJgeZn5ukUSg1s+wOOGJyMBgjZ23Pz9ddRZQ9CFqfIY/bOBQaGrYrBYIB6vbZQ/4anfUz5GcPewAbUA2qWvQdto/kFBBR7RRwpLbymtbEB1/fvPoDD83Np/QzCmKyLansTHkM8ewe7GE3IY3f5yhWEIUM5xmDnMa2RXn8IyRxOrlvB8kqRNaZwslDvwM9MINiazfMMLs+PUnKWTCAMHN4HjqtsiKKBgCpMdyOJqwOAUsL2nbK32HOlcxsTf2Z5Bfs7BLu4vgPFwZ2HRyfY3afA12FikGVF1psDj/miNBTSfHHo9fKF89a2U3OwXa5hebmgYQMxl9sdrC2zB7pexcs3PgMACCeR9Yz++IN3cesOZUYeuQdot9hT1KkjGRU8PGuonCHP1epSG03m6pGZhuZ9tlRt4OUbNwEAjWYTksehPx7huH+6cB87nSbeeef79m+Ls1BnCVotsvBXV5Zx2qOzcDQeoNG6RONgDHrsQe8sBaixp/zBeydYXSavzkbHRZzQ/huPJnADTiJJFFz2nvVHY7RbhVe1jq0D8qS2lhQ8znSSscEyB6VHoxDjBTN8sixFzp7CTw4m+J2/Iot+fWMDV68TXDU6AIZ87pg8hiOLBBWNdz55CAA4t7KCSys054EAIs4a3Oue4uiQzlxfSay2aT7TKEde8DDlU9Q6NJ+X11ZRS2iPxplEiz27gasQ8V2lHOcJ3ptniZEKjSqNfSAFCk4egxmXjoCwZ5kQQJFHY2ZIFIQU9p7LdTZzPxhj97TJ9VxAr7KedF9rGJ9hnTxDzAkHUgCNCt1DFd9Dovl7ZeCYxX08/0a2E//T9zx0Gdr9zne+g08+ogzkn/jiT+CNNyg4+eTkBG99j7jn3vreX2PE574xBhX2PjXadeuxVo6Ek3PGnJCzl82J1rk9qwQMCpozKSVUwStmzByM/+ny1FleXV+HcmmT7B11sXtEC6fe2bAR2aNxhKNTJuGKgZwPTWWAB4zXne7ewsYS4agXzp/H0gpBXYHjIOG0xaBasxus97iHCh8oS4EHn7Ms8jjEgz1a7Pv9oXWdNisulut8qeQxlpuLp/smCbDziPp11I3QbtBBcPvju9jfpslEPsAPOfbi5ue+gdVVan+/P0DOEEKtFqDSpjY015ZQ3NXB0ioO97cBAIfDh1hiQreKO4bj0DOl0EhR4MkGCSs8uUghPZrAi1fPYPUMKSTDYYiIIZZFpNluo85jkhtt08bbzRqqPI9COxgNuT1OxWan7O7sYGtri34DYbOxlONhOuUsjtOe3SCtVsumtC932phwVlIahRhxpl612cIrr1Icw8rqGsJxEXPjIua4iiRJPjUt8tPk4KiL4YiecToYIeMD1/PrcDhLhFylBV4OS0DmOhKKlTJjgIB/H7gVfPRjytILpITk+JzG0giT+C4A4EfvfoCvfY0yrDzp4GhI66g/GuCTO7R2Njc2cO4MQXl+EMBhxaPX62M0HC/UPwDwPM+60cVcRocQeub9FoCQBRw6S4U1hrJbADoshJilXReHrNHG/iZHjiGnJ1/wPARZkemmcf+YlB+EEwg+XB7vHUDVihgPjUvn1/g5Eg/2DhfuY8VvWCK2+ZTXTMzFKEigzQrJ5voGahU6QJV0wDYGOrUqimvl7NrPot0k+OZvPvoYCcOOgd9BPOL0eaGxfJYu41arAcNQ1+C4C5kWmT8ugkIhWV3FgCF9IaVNwV1E7ty+gzHDGJ7r2rXarFVn8VfCAHzmdVp1rLOS3O0lyFKm6Kg6AJ8TSkhEE4Lz9DSzcSRutYWUm3YyCqF8emYYxhiNOZvI9TDNaK43GhuocXaeCyCe0LkuHG9hhSfODSaG9tM7Hz/E1hGt8XMXPIQ8Zvv9KYYhXYKOnqLK+zI3woZE/MmHD/D3vkqxkpdqdZyE9P53Hh3h+JCeWfUUVoZ8RkxDVHiPGreK0ZDG4+UbL+HqmRv0/PgAiteRn2cQgvrtus4TWVLPElcIe+FSCjUb+fPPmFu/EDN0yxhjf2dA8UAAKfE2XX0efJoj64PRFpY/Pj5Ao017rt1uQnO0g04zm2nqzWVw6tzAPEcMz3yKtxDCUlzcunUH3/72d/i9bXz9qz9J7Tns4b/99X8GALh9+zYGA44vS/Vcpq5EltK6i6LIUmsoJedS1J9UeD4ti21eqTHAjFR1AYWnhLRKKaWUUkoppZQXXp7q4XnYDRFzJsB4PEXIAXPe6NByIfiBj3qT3HvT4RQpB6C1Ax8xcy1IGCwvk1dkY2MFU/7+eDhGzh6DKBdwaqTx1UWGjTo9c2O5g9GY1NdHJ8foFRpiIuCx1tkfTjAYcvBiu4bMWTxo2WhlIaRJeIAJt+e4/yEcl0sLKIn+EVkMvYMf4zNvfBUA8KP3u5bgTPnC8tJMwgQRez+U38TyOmV4jU9y7OyRO3C56aDd5OBOOYWRpJVn8cSWzIASUBxgKpRBUATuVeuWVnwRyWGQML9JtV7DJQ7KfOXGFewyp9Dh3iuYMMlhbxQiTqn9Ww8fWg/M9ctXi9hX9Ad9y4UwGg1tkOhkPLUlIaarE8t1MeydYnWdPFSPHm5hibPV1lZXUWWPk1cJ4PF7a/X6zCx6hjw+OkbM6yKMExusW/EAZ85KGXH/RpMp1tbI5e1XKwiYDCWFxAUuebHxcBvv8Nj80Z9/F8phYjrpWWr7tY3zWF2l53S7fRwdEdz3aHcL/QEBVkudlg3mS7PUeshGo6HNylhEolBajgnK0mLvh2NsgLSUCglDb0rlUN6c1WRjJoX9W/KmFMHbzozMS2rss4V2fjxBk8dnfzIC2KXu1Cu4fJYCZXce70FH5L27dHETvqQ++rU2fHdxm0q5nrWKjTEwPI+OzCHywiPnAAxxH3cPoZdoHVW9SpE7wRxEzInk+fjC668DIK+e4OygYW8Al73XjdVlSLa00zRH7xHBkft3HsIvyFaFAJbIg3DhlRsWWojixK7lRSScTnH5KgXD11tN9E8J4vZdgyims+HoaA8AtScOc4y4xMl0HCPl7CBZX8eAg94vXVjG5jqN88mjHsKI9tNgkiDmcINaxbVEjtWgBofPmHA4QouTBmQqoflMzXSKIUN+EBWYfLF5TODiLpMmfrR9ipCD1t/94BaWOHO13mwg5flsugI1hoiPRyESQf3+cHsPS8sU4N9+9SUMxnRGn059jCSdNeNUY3+X7oaaSIGMuYVaa1hlgtpqJUDs0l0yiCtoO3QGSMfAY1g4mU6tt2ERUQI2CUBIAT1zwdjfSCFmx9fcOTbvtdZzxKxRHCFgb6V0vFnY7jwHjs4tVKt1bvexciSaTHqbJzO+vCiOkFoi1wyeS21mbt1nyhOwFj9z68EWblx/CQDwxhufw3/3T34DAPDnf/q2hcp930dmc4i09c4YA5vokovsCc9ooU9QMsnsvZ+Wmaq1tiSgWZ7b+5L4ef4tIK0PHw/AHl3oHHCZzMnXBhVmynWEIPwRQNVXaNZp4JcCH+N9GoDlK9dQ5U01ngxwyunbozCGX5AYQcBjV7IvfOQxPX88ypEU+KfjItH0TOM5SLIiAydDNKLN3A8T6Hzx6IjjwxOc7tLhdbJ1hGprlrJb1Atrr2zAdemy7m0/wEs//4sAgPv3dmw6dtUXEIwxdg+61i0Ot2c3U9UNkGWXAAAxJPo5TX4WbcEVlBYrTA7BMR/GCEuQFyUTRDzJcaZtvapFZDgeobVMh7Kaq4d0/eoF3GbY5sfvfoTVdSJdvL+1jaMejaHnulS4DMDdOw+wzIRrYRzDY/LAyXiKMStFnY5BxNl8jw8O8NorFPfQ7Q8wZZzZr1WsEtDv93CWySSXqoGNTZlOp7Y+17NkHCVIsqIWi0bGF6VMUzRYqegsLeGoS7EcRgp0Vin2Y3VlBeMxzeGDDz7C6gZl7GWZhuY4nzCJkQzoYvI839b+6nQqqLNi7rqB3ZzLy0uocWxBu9O2859mqT1EsixDpbI4u2uWzRF/SmmVE2MwV7sMFsbSWiPThcvYgUCR5SIsOSjMLPvCaFgF1ggg4r/96OEjnDtH62I1y3B6TPEq1Ubd1pSLwyk++3mqA1WtuUDKCk+1ZlOIF5F/PdvJzLWtwjWTGo0GDg7I+Ijj2I5JEtQRuAxHBgGEU9QukpYk8Btf/So+uUXrvdc/gcfwpd9pWLgwHI/QP6G5Hg8GGHJsmJYCTk5nTHDYwpghp2k0xfOkv/zsz/67tjZZmEToMcy7ttxAn6kgKkFgHxlOFN79EbXZOBpVTvPemvRwsk9nxri7j5cuk3JwpraKoEJ7fTQZWQJGmRm4msawUvHR5vifqDeAYIhtME3RZ3qPxtoSNO/F3iSHnCw2j5FWuL9NZ8fRYILGBkGFp4cHOLdRQJFt6Iz2tpcNEfD9obIMJ0Wtq1Tg8IDjBTeWMZ5yrShTRZVrow3HI7gBwaeul0PEpPykcYyb1ygD6NXXPosuM6UImWAyZroNkVil2JHyuWJ4qp47o3kQ2ipvjnBnLME6J+UcgIK08Tw6z2eVCbLM7lcllQ0FcJWH4tI3mNXbgtHI2FlQqfhQvMZNllk2acdz7NrJ8hQJr98ojjEBx38tn3lmH+cJDwFgOqa/ffeHP8KXvvQl7q/CEc9RGE4RMHWHFNLGcQFzENQcs3Qca5ye0lmS57lVYIQjLIGvMU8hP5x7dlFnTRvzTBqMEtIqpZRSSimllFJeeHmqWjsyPsAcEEpJC6lUK4CSZK2HUWwJ6HSuwegWonGMTpusDsdT6J6QNjfNxgg5IDKKDXkcADiuhMO1iJIswc4RWQnDqIN2i6nPpwkME2NJR9mAP+lVEacMSyUR5HNEo5+eHuPRPdIQT/c1ltsU4Pbqa6+it0HcO0JMkHDdnd7+MfYeUV9Wly9g/4Ay0U67e9CckeJKHxfPkhb92cstmJQsj0ZtDY0macdhLNGLyVXdHd5BNCH4xBO7SDVZblk+stXD89yDNkXl29jWQ1pEDIAHDyn7YWV9FQF7285urmGTiQe3t3Zw0qV2nk7GGEZk6V04dx4xBwQf7u3B4fVwOuyjwvwmAgIOQ0H1RgNVXgR3797FY4Z5Kr6Lbp+sqw4M7t4mMqrRZGzr7dxUEi0OSJ1Op7Zm2ZU3ntFB6c7IL9PUeng220s4f4H4h4Tn2QrXr33+i1g9S5l5Ww8eYsLZgduP93DSpznZ3d4lDiIArhS2dImUQGro+17vCGFIz1xdaePMGXqmUZsQTmHVACFbR1prxLxXKCj3OQIlg8QGkkspbaVspeZIyoSBVLO6MlAccKtmQcsQM+4gqVyb4UDDx5aSEFAuze3JNEL4mLivNlpL6KxyckC7gjEHXQutbQZeo7mG6ze+CAC482AXuV7ciyWEeKJMhq1pBWMzApMksVbieDy2v1lqLmGDy8L4vv+EB8zGjkoNyMIaTEGhuUCchHAjDu4MxxYKrLaqGPQJNpKOQqVWwF6RDfTNdfLM+j3zMhiG2N8nyGxn97Et83GSz+qvqbqDQY/GNuw6mExpbJtLNWgOtK3UFNZqdL6uijYqhsanIlvwahRc3TWwpWNGoxEabfJqOkLhcIfgn2g4QqtDv4+nERIuz2C0gMMBzEcHXTSri2VMGghMi7pnWYK6x+e16+CzL18CAAgR4+o5gvUGJ1s42iF+o3OtJhK2+g0krnboHNFRjFgUZKYVeFxSYy9P4XNfq0oAjJDs7B9hws7hamMJkeQrEFKyAAAgAElEQVQkELmCwYDetSxTZExU6SoH1cqzCtjM9THPoZwiA0va0iKZ0TPvSpZZryGkmWVp6XwG0yhl16nne094M2wl8TmPkEBuvTpCAjl7YY0GVIGriVmmKwQsr1Waa/SZY2wReTJhxGDCXvA//sNvw+XEiy9+7vPI0uIunyDje93LZgkWWs/6QvUWZ0HIBaQVRdFc/b35QGWBvy1xpTgD5r09Bnhm8PlTFZ6g0oRyaNHXXAVf08JZbflQDqdEPtpBrmdu9BFfjlOdQ/gMW4RT1JboMnDUBG5I2SzKE5YsKjfA8QlldPRHI3hLFIFeM3XEXMsnnyYIJC3wJMvBaw7VWhUjjuLPhECeLY7HpmmIPrNGh4mLvWPKLFt3b6LSJOx/Gh5gMPqEPseH+KO/IBfzuVe/DH+FXKdOlCFn/LlRa6C+Qptyaa2G167S92ttFyalRn/wwT6+/w5l+yS1ZVy4RNCPGPeQRqQMRPk7AEgRMiJDytieznLgOaiyHMezjLSucm1KZeNKE7/4ywTPvfej97B1fwsAcLx/iJw3yj72kTDjapblOGWlZRTFOO7RYbrcWULBpvV49xCXLpIb+8L58xhw/NXmxpVZjIUT4NEjPnCjFII3brPmIeeD0PN85Asqro1qAxGnk6fpDLJZWVq1sTrb+3uWMNJIwGEG8Vt372J7i9Jyx+Mpjk/oguuenCJn6GGpWUGFfz+aTCxZWK/XxWNmv65WW2i1CDrxggqijMYsnIzhOLNCn8UmT9PUFtJbRIw0UEzhoIRBETiQC8+yqUJnyDl9OxOurWkkpbCEZbkRFms3Yub+NgbWuBHSRUE+rh0Hw4Jg8vABVip08dUmNdRqdEl02g2csoFy/doN9Dn7aXe/j0wvDhUUBWipzXJ22BmDAcM9cRxbpRGYucsdx7ExUXmeWxixiNMAgMlkCFuTCzlirqGXphpxSO2c9rqYHNGl2z86sTEH0nURMoxVVylyhiOl1M9Vu2//oItBj9bYudUzlnqh2zu0afKTQYQGx500qhVIVmyUo5AzRKgGwAYTDNZaFVS4EK0P38bqrFbqqDJMfXR6hKBNZ2ez1oDL60QkORT3pV6RSCYcqiAl6lWCKMzSKtbXFiseWnUMzjZpfe0hwcEu7a1qMkW7Ru9cb9Xw6hXal4/cCbqsAFaCAEspry/l4BwXFe2GI+yysdLYvAGfMxHPr7dx9hydNYPjE4xyUgwhhS1s7HgeHJ5DOA5iryCHzSCYBFYKOVeM8tmSJrGNgdI6swVv5zOqHOUg5XhEI+dYjpWCKGJvcmPPTUfOYDIiHizgZWMhUwFgZunOYtwwV9MqN7D7m+KIOJ4x8JDp6sJ9NFpbpSLNNLYe0Ty2V5Yx5jNsZ2/PGoU6S2yKvdHSEpoKCFvP0FHCkhAKSDSYakAYhUIR0jq35KBazxQeA2HHSoiZwpNrM6urVUJapZRSSimllFJKKc/w8PhSWUpslU/RcMlqavkBTtkakdK1lPRSOZbjI5MZDMMcQaeNOnt7kuk+fK4GLg0g2NLY2zvAiLX41bVVnFkjy6TfHcJ4TCrY3EQkGTMLQ+uGDOo1GyibaGUj0xeRLE9s5e6kKtDvvQ8AOD39GIFHcEireRGVNnFCeA0Xt3e5vAKOcO7SRQDA2fNryFl7TVNgzFrq9+5N8NE+eQQ+dy7HT1yjd13YzNByaAwf7C7hwufJmumc28Qnd0jzHQ8aGIyJulvV3oXg3zuiZrXaRSQMp9bCWFldn2m5QuHaDYq4b9TrWO6Qxei9/wH2j8n6GR8foupzJfdm3WrWgetakr7jkxMULgGd5ZhyZsjy+ooNej866SOLuXZRnJEnBsC9O1toMIHKhQvnrNXSbLXRbLUX6p9OtK0t1qg20OHMnc31DbQYVq3NZZK988Mf4O0f/QgA8PD+I+w8Ije3zo0t6xFOp1DsIen4HtrM8SHSHH32/CRpioNDsnxWV8/AY/JIIRrIM4bY4mwOXpmnR5eo1Re3uNJEUokIANIYGA5mh5+BERg4Op8lhDjSxibnuYaZ496xf6siWwtOCIWCr9EoRV4kAFooSM4YmmoHHz8gTikxHuDV14noLzNAzPwwtx7uYarJczkYTWCeA7YTT2SzzHhJdKYxHDDpaZZZl3e1WkWrRWu20WzYTMR8MobL/FLKdZEwyV0YT22AY5ZraOuF60Ozaz6djJEyBJkhh3Z5ED2FaQEFpik8rrPmKe+5PDyj8QmMR89pnWmjf0KeMZOnuLRO5008mSLq014/22nZPuo8t9ksuTZYYt6yPE0Rs0frg1u34HJG1eaZZTgNaueJ4874tzodSN4vSZyg0qD97daAAcPsjorB8bFQ8NFpLy3Uv5rM8fKrlwAAvkzxJ++RB7TdquHiVfKGX1rxsMGEtnm6hlvs5RqNpmiwl/Tc5jm0z1F26872PWzdo4SDN86+BJdhnUbVx+YyeWwqIoNOOKvWc1Ft0F50HMeGSriuwMSjd03jvr0/plH8XDw8RhiLIug8s4HhSs0YBh2lrAc3Ewai8HS6vi0bIfJ0lv2mDTLmiDJBAMlr38i5elv5rEI6MAcVpSnStCgLI+AVoSeuhMPeX8+RWG4tBksCBL0V5Sr++E/+HL/zu38AABhlOd79hOpNnv4v/wJjRkdWGk1LYus6Cg6HAFR8D2lKnsLxeGJh/PE0wd5jCnc42Du19fpcX8Nxi8xRBeveMgq6KCNicqSclZtr6/iBcpxncrc9VeFxkUNxynZNJDhbMAmv1y1213ed2aEmHVuAUusYOccE+G6ApHC5aQElGDuPJjg65Xo8aYq1NYq4P3/2PNaatMm7jw6xy5H1G9eacBnXbSrPMlyGk5G9zCQElFrcjS6lwRtvUIbJj9/bsYRVSqaII4pdOJhG8AJqz6raRCUghS06PsQj9q6HUYb1C4SR+1UfRZZjFgfYPqT+7j1O8eF92pRfuN7Bf/Qf/z0AwP3Hpzh8TApGUG3j82/Q4dKofgFv/xUt3pOTKZpLd3g8o+cqkPrKq6/ihz/4AQDghz/4AZY5Y2vQH2Jtk0jxGs0mLnG6bJIkMJqI83RSQZ3xe1cqS5YnYsCwK3eQJRhxzI/j+hiEXHtpe88yvQ4GI3Q4o8n3PWiGZLIUGAwYMjkZYWWTcd1wVjT2WWpBOI2gGIJbWV/FtZsED66urkEUcI/R9uJ4/+OP8fEdGktohWhaxKAZS7CVZTGyIgU4M2jyBXoiDFLe2F5urBI3HY9hltnNmsYwfNiZTMPnWIgsT6G4PY4rkXDc2SIijUJmCqZlbVPRTTpClUk3V2oNZCG32URWYXBcAVEUTsWM1M51tI0LijKBkONtNLQlr8uhYVh5rwQeaitFll4fH3/wHgAgrTThNElhP5q6EDxjwgngP0dRRso4Y+ZvR0BwvE2SxDaLJs9nMRDVatVCVqlOkbF27cKHYiMmzrVVEqZZjjDmOMFEQzPkIEVu++j5HnxOwzdGQHJatYaC06DLtdFuW9e5gZnR6C4g8XQIh+v1jbMhdDEXQmJ0xJDrxODqGTKkXr5+aRbLJATGBbVCGCMtCOakQm9EMNlpv4uXz5BicaG+hI8GBBcNRhO0l2lNPtrZxqRPxmWz3kSFC4M2lzw0lsjQPB1vY8z7+GDvFJsMjT1LKp7C5hKN0z/41jeQg1ilK8hx9QbFR8ajQxiez1qlgnqHzrvHpzt46RK1/cz1VzAsEM5aB3WO1cnzDFGRPRlG6PF51OwsIRjS2bqymaLNdfukI+HyPVQLgNPC+KDANn7mDL5ZRHJjkBexKJ6aZRMmqYWTHCWgOUNKOQIuryNPucj4jsx0YmvfaWiEXH8x8KsI+I6UGrO6htB2/UZ6FhcUZRkmTBkzjTI0WJFLowyNFp+50oP/HPcihECP49e++93vYcRrYZzk2HlMSuy9+9vYZPLD11+/ib0DpgWo19Dk71++cRMDjov8y798GwWoNJme4F/+9r8EADx6+AA3blK87KUr5yGdYl7ymU5w4bzNAovjBEmxd6WC73MhX8exWW9/m5SQVimllFJKKaWU8sLLU1U+JRKcaZBOdH19CRfWyHJrderY4Gq379/Zwb09skyGmQsB0ii9LEaakbXuCiCL6TfReIxxj7wZJ90TZOymOrNxBheZgn+92YbH2netVsPdXdIcB7ffx+VrLwMAAs/HkL17U+MhN+wSc1zLObKIVKo+rp0lz8b+Xh/9LqnNnueiXuOALHGKPCNranI0Qjyg9lQ7R8AKeRMeTCc45sDKGy9t4PpFskjcYY6Hj+iZj5MA9x6S5v7u9hCfP09W1k/9ZBWXLtL3H72VIWU44QuvL6HVpOf/b7/1EEfb5ALcuHAEKRcnV2zUarbOyb1793ByRFlg/f4AJ5w9p5TExiYFlt987TM45ar0WTQjk5RGolaldoZpCo+16Ua9imxKbY6SFDlbJ2E0gWSoI3AUphzpXwlcKM6QMWmOw31y63/i38bmBQpCPHtmw1rmzxSpsMy8Otdu3sDaWbJGR8MRhgdMTFat4jUuZ/H+Rx8j4srw4SS20JzWBlWu9aJNigkHJ2uhUGUvV5qnlpTRncbQzBuSpQkGfcoqcxwHEWdvuVKgxnDuSe8YU7bKoyxCHC/u4YnTGEUpa9cFlKH2X12v4ed+iogwV6oN/PC736PfD4fwOegUMoXvMamZcKyr3egQE66ULf0m+lxbaBQO4HBAYZQnlmdEZBnOrpGHRzSv4S2ulZfnBkZwiRIRQJmCOl8/0+KaF61nwcbK8axLPU5i5EnB3xFb7qM0TS1ZJhEqFsRnFQsjzmd+wRjLjSKltFXdo2iKhBMjoI0tO2KMhOuQpyio1uA7xfi71ss0T8G/iPiOgxvXyZqdJClitmZ7oylGJ3R+bLY3rPV+995dG7A9T77WG45Q4ba98dpnoWo0/r9w8yXUJM378e5jvH/rNgAgWw5wyBb4ZDzBy+wFVVJhXHgyQ4Nmk4KTV9ubgKb1HIXHOD44Xqx/voec18vZpoMvX6R9UxGAjsmDMZxMkdXZ++hV4DcJdh7rXTQ6tI/brRb+6rt/AQCoNQWWGOZIkwQJaC3ferSPB7sc8OwHMwhJ+bixQZ4BKkVC49TychwXtcLCGdGmUvK5iAdTPYOIkcMG6OYw4LwCpKnBHmeonnaP0WRofWNlDRX2IAatBgR7q6bTKTL29ozSGII9l1IbW7cygcaIA7DjLEXO6308naLHgfBZDrjciK27t3DzVYKd240mXPc5CKOMwZSzZ4+OjsBJVJjGOTL2zieZtlxsr752DZ/9PK2poF5Di7mvXrnxGbz9FqEL3//+D2aEuTpHxp7y6WSEhMuOTMYR3n7nHQDAvfv3sbFOnuNf+dVfwhvM9ZWk2QxJMilyrtCTZTm6XbpLLl1Y/9RuPVXhcUSON9+gAXvtyioUM2W6notKhS709nILmzt0ad7b62HngJSZPAEUHwpJMsGoz5fs0QEGp7QQII3Norlw9gxWud6TL+gQAoBqvYI2F8bbOTiGt83YuatQb5HSVVk6D50zbGCchRl6AaBarWB1jVyqm2eWcfs2XUIZUiiuoOiLGsDxSNrLICX1MZzsQjZISWv5NzHionYf9rZQi+ng+Mab6xgomtiHDxNEKccyjQN85z1aRbfuZfiln6GF8KU3a/jkA5r87377LXzxTVLwvvaVa/jN3/pdAMD6mSYSdBfuYxRFWFqiPl67ds2me1+9eg1DdgPXmk00Vmk8dx4+xM3XaXFF4xFidmeOBgPsbNMBM02mluxPuQ7anOrez4bI+CIXOkMWFzEHCmc46+Lc+Q1EE2Zyvr9tY65WV5bhcCzQdDK2uO6zpNPu4ObLrwAAVjbWLU4c1GqosCt85/Fj/Om/+lcAgE8+uYWIaQaSOLUXXJ7nFn4CgJQv3N5kgpAP8e5kjITd2eMwxGBAa8F1JEYMK3iBjyGz4FaDBmLe2MPBEDmPWae9BNdbnGk504GFak0e4tp5Gst/+PPfwKvXrwIA9h9to82p6F7TRY0LZaZZhmWGGWq1hk0BjeIJspQOpmq9je6I+vj+rS14XIhRVj2buaYAbNTpsvFW13GRXdXv3d9BllEc1Nq5GtKM/tYYgeg5GMGDIIDvF5liBhHDpNE0Qsgxg61Wy8ainJycPJHKWxBVzivKWj8JV8wKpyqkfE44rmthyiRLoIu4RUfZ1GwvCJ6Yr1ntH/FcCk+90kLUo74cPHoMM+X4qGEGycSuo2yKD7cIcu3Ua2jzZZkaYxXIa6/cRCvgAqCNGqYTrmv2eAcPdslYubX9EG3ec+1WDScn9H21VkPCl2gax4AuDDsfkyGtsfX1JkY9ik8bDQboHi1WIFUphXBM58ukD3zpGikeSThGjw2tzKtYMlntejgOWZkVDprL1N6w10PSI+jEr6wjZ6hzGk9RrdFZtnc6wmGXziNXeTausb28ijc5u1hrjU6N+nRuuYWjfdqvwxMNh8ks9TizGYeLSBQliDjDr1r1oVVR1BI2U/C018PvffsPAQBvvf0WYo7/Orexjs0Ojcm5M5fxzZ/7JgDg4oWLiHzaf4lJkXPcEZSALuz31ABFkeYkRspn5aDXx0mX5kcIiWTAFDDjgVX20zRFJhaHtHI9Y0jOsqwgzwdyDYeVtMxoNJkIc2NzFSvMpJ8D1ijcP9jGhI2//qCHyTjhsTL46Z+kOoSf/8Lr6HZpbTzefQyPjbOvf+0bdu+6ros+w7CDXoQ4KVjAE8QRzfv9ew+wvU1r5qd/5s1P7VcJaZVSSimllFJKKS+8PL1aet3FZocskFpFoL5SEHu5yBgSWG56ePPzFIx25UqIv/4bcnM/2NnH4Qlrdr0jnOyTdRGPRqhy8FGz3cBFJoA7s9RCrSBV0qmtrJvpDMsrpNH3wwh9hg1qvoONDdIoA18iTznIMnNngaoLiOcaCJBGHLghciYJlH4T0hS8Hgmg6HsDwHBdJSWGiA5/SH8rBVbP/QQAoDsyeP9t0jSnozHaL1MfNy74ONwmy2ASKownTDq2u4vd22R5/P2/u47/8OcpW2PnQYrf++0/AwB88Zuvo9Gg8Q/7Q7i1xSEtOeeyXVpenlX1NRLtZXIhK9/DUZ/GodKs46XrVAXXpCk+fJ94h+ThEWr8+8d/+hYiTuNYbtUtLbrXbKLgCDodD4uqFPj6V76Mb3ztKwCAlY2OJTj7wfd/hBZnVHzpy2/A8ekPJpORtTCelR/yymdetTWtpHKoxgKAaqWK7jG5OH/nd38Pf/SdPwYADEZjZEwSGcXpHInVjAvGADD8/r3TLlL2Wp1MRjb7UM+5fYOKB8kQQxiOMWb+oTiKLdTieS5Wa7RmncB/Lg9PbpZQeNE9IfHl14jA8srZa6gENEKPdt6Fw8H1y3UXAQfHCiWxzlmPyytr0JzWMBxNrGejWqlhk9d7b5zilLOE1pbX0GIvWTKNUeFCPHe2dy0nk84iDA9pvS+3luDV6V1RavAcFVAAzEjHdJ6j1ys8qRO0OXun2WqhyNxoNptI2eKdTCazZAVj7HrXOZd/AHk6bZacmlWSdpRCrVFA8ZmtF+c4HhR7ulzPnys/IJ7IvHseD08UG2w/pLMw6aVQeVF+wIHH3iQlDept8t58+bPX0WiSF3mcS4SDLQDAlfUalrlEwGmo8d7ufQDAhw9v4XhCa3Xl5Wu4cZmCn+9+/B42z9A68f0qdrYp2+6lm68g4DS/0/4xtrYpA2f7voasMcHjZIrd3d2F+ieFsCVHAIMmk5BOdIKIvaS5t4IhB/t7QmCa0VhW2utoFR6e3fvwmJ+r6jgI2Wu7s7ePFsObVc/BlXPk3UzDMSL+vlFfQZbwXRJlaDRpXfueC4BhZJPYwPNc589VWiJJMxw/puSWwHWxxNmtUkmbyHH0aBfDQzpP60ENEWeQRTrFnfsEM/7grbdRCThY/mf+HRi+jrVJ0OuRx+Ok28WQiU5XO+vIJrxfazV0OMszGnuY8J0KbTA5JuhyuVbFEhO5uo5rOXAWEa21PbeyLMPeNq/Z6RQZe8dNppEXnGSnPRwcE3IT5yly3kTrq8t0fwIIwwmmIfU38F0EfP6NhwN0jyl5STse2h1q89mzGzZw+uj4CFN+762PdyE4pGN5NbDcdvuP97Cy9PS6dk+d5dcurkBG9MLjx6eotGlQVdDGKZPOnR6dwq3SJDQrDbxyhTbn9s4D9E7IJXp0eIAJH45nlldxdoMOxHq9gmWO5vaVgcPYY5rmGHJq8zhJLJNzlsXIGH7orK5jpUmKR2wieLwhEungOQLuIRBje5sww37vMaQmQDAaC9T5clKeRJbTOCSTMbKMXMwiOUB8Sgv/5PHbWBn9CgCgtv4VPLj9IQDgYKuKxoeUebDxmatwA1IYRDqFOaFLYnj6AU7HdKD8xn+f4Nr5fwAA+OrXX8P/9Y9/HQDgneugUaeaU8P+29hoLr54M6NtoblcAEsc71KpNiB4o7/z/bexyyRhb37jp1Bt0GZSAEY8F0fdLi6epzZcuLiBrUe0wL/y5k8gizhWxq8h5E3w7rs/xrnLdCD9Z//5f4pak9ZPnseUbwng7Pmz1k3vuMpmLuVG4+SYNv3FZ/Tv/2HvzWIsy7LrsHXOHd/8XsyRc0ZmZVZV19RzN7tJyqYgmIZhAbINihL84z/bgCGDsAAbECDDMGAY/rH/bUGAKVsmbJMCZUoU2WS3uovd1eyuMasq54zMyJhfxJvvdAZ/7H3PixS6K1/C9ocTdwMFREXeuPeM++yz9t5rryyvQmBuxAmOdUmSDP/y3b8AAPzFez9FwVk8RsMRuFHsR5nuOE8bV0qh5E2dFgWKEzKcMl9C8MEaeIFzhxltUWcFdHx8irBkOM2VY0RdWVlCUrrPhHXurUXE2ikKVuJhvYYZK76HT48xy6mdT3aPsLFJo9WseZBcO6fRirF8kQ7HpY0LLt5NHp8gnzLpY2sJJqTfd7ef4mRM67HIBBoxwdadVhuDEX333uPH2N4lxbrfH6Dgvjx5eAvrW0wCaixehCDTO1PkNM0yJByfE0c1NFqkJ7Sdu5CEH0KUOanautpYgMCEY5PyrMCEa/cl2Wweq3HGPaQK5dKAQ8+nGnYAJHzMw3+Ei6tRqnCHpXeGLXcRWT1/ycXHmfHMUT5MxqdoxmRYCq2w2qJ39uITnByS8Tn0O9jscD06fYLTY2pn1lrHzpT2X/PiCpoB03XUVjDmC2LsW6wu08Vr78k+jjkr9Gtv9aAtvdMP6qixcYDRBEvLdPDcfP01CLXYJVII4Vixa3Hs4uCyQiHm9WgjH+OU5rZjMrxx7QoA4JWbr8Gwq6JTD/Gl18it3ljpoZjwutveQzGjvbjsRVjlrLInycjRD4QyQr1B66XuKUR8Uzg+nSLjNHADgZTpJXzffwFTABicDvDxzyhD8Xt/9M+wztmfke+7tZmlGRIm6PNCHxfX6SBe6naxvEFnZHahj8e36ez5XrKHRkxGPaY59h/Q2XB3+xEGzBq9srQKMCP4b/71vw7vGunW4909aC6E61mL3cePAABJLcLjVSLS3bh4Ea3mglVDQZeDMgPy5o0b+Nl7dLGvRyGWmEagVW/gK29TXGSj2cT5JWp/o9OGx0WD6/UQn3xINBVUn4veXxQ5bt8m47rbbeLKZdJbUacFxWd5Opsi5gtwkWUIe/TdOGxjyuTFh/t9dJu0b95+843npqVXLq1KKqmkkkoqqeSlly9EeJabIYZDdvH0GjjcZbdUK0PKN8PIr2GH67LsHd3CDgemffT+z7B7yBw7s5mzwrYuXXAITxT6Z/hKFFIOEp5lGhmTnc2KAkd9suaSyQRLTEa3sdxDkwMcTZYiYBdFBOuqXC8ieZ4in3LA5TkP3/nXiOzq/r19nDDM5quOK7Hh+wJa8Y0xGeOr36Sg7gvXtvDDn74PABjtTBFa6tfwwTHGT4nMcP/OOSxdeAcA0A4iXGkQOpT6BziSZNE/uPsZfvf36Tbwt3/rb0IwVHl6dIDNdQ7e3slfKDB7libQZVZJMnN1TkbDyZlqygabKwQni7xwmVzSGNxkcsJGXMP2A4LOA9+H5MycVqeL179Ot7FsnEKyVR5HMe7eI+j86d4urvdobLNZ4SoAx7WaCyBOVUbkXQDCKHIB7c8T4kopbXfpmKj29g/wLvMPHRwfk7sLFNhX3uJh7TO3gvLmrs7UsLEQxNsBAGfIsHrNLi5duAIA0Fqi5LvUWriq8pPJ2AXfhlE4D6D1A5eFtIhQ7Sy+ffsNfHybaqPNZkOEXC7hYPsBatfoptRrbGCpTevICy3qDSZiq9UAvuW2Gk0oJvUsjOdIHzfXV7D/lNZ+lmXuxnXSH+LTO7TXJzZCc53mc7mWYDYjRCVu1hDVuSq39/zaNmdFYl74ocgyWEaNolbkGPX1Gep4a60LwI6jmsuky/PcZW+pQiFn16tWyqFqZ8tYGGtgVVltOkSJSp3N9jQQbm1Iz3NrRinlvrWI+EGMgBM+guUazJhQj3azhYwzcDrNhiNwnRZTXLhOyEzDa2IpprEtBhFu3SfU+dbBfYzL2mpaQ3LA6Ge3P0G7Ru28uHkOUtGazGd9tJkkMzns4xHv9ahdQ4/d5qfJNjpLhPA0e8soZou67awrqVCoEIp/nmQZ6owktfwCY05msFrh2ga1S7YaKDjYP5kZKJ90353793DE7vZidACPy7wIv4nAMDrlW1xlpOid7/wagqisyTZGomjMdg4tEsUkjogQhWVwvXElXxaR/+Uf/E8YsKv87t07uM98O6GduzojP0DAqHoQCtT2CPUaao0h78VOzUee0Fkyfu8uukwkWTMKiskvu1pDsz5NR1OsrFLAc//xNv78TymL7SRPcOlLlPm3sbaKGbuCTz8+wOefUPD763/11/Gtb1JIwSIckp98/LnTVW+98Trsv0eccUudJs6fI7Q4jmK0G8yNMxsjYl0S2PEtQm0AACAASURBVAIpBydPxwI5k/B50gfAAfKW3HUAME1meH2TwmKW1lecayxAgRXmaMozDZ/3342rq/jDPyR+p0tXNnHjVcqyXllZxfMwnC80eJLC4KRg94AvsT+iiPhWdIhGScS3eR7nGZb94PZt/P4fUibR6XDs2HejOMQlThVe7rVQZ0PFlwK6THHTAjMmWJraACYiw2Y2SHBySspUGIvNZdoc60vLKMsmBjpBVGYaIIeyJbT9fKHiY7TY640AW+yPXV6r4fNPSOk/eThCmpK7Iq71HOGhjLv4yjd+g57faOHp0/cAAJ9/+inWeAJ7Szk8Tu98snMfOwd0UHV6LYh1UpRJqnB8RH3Uqo3P7tOhMioMLl+nydy+dwc3rpEB8NTTzhe6iGxsbED6NOZ5mjq3QTKeweNDfXVpCQP2l05OTmA5XTYMAsRMZLW+sY4Wp203m3v46BPKevrBD97F0R7BtEvNLl59m9IT33j7bfz8/VsAgPfe/TEuXyN3mC6UcyfovHB1vojVm75bq9UQsJH8PBFCOiNOCOliMB48eIiPbtH3R+MpJBvCgR+Ck1SIJLA0bM7UYrHWOneJgHAGphTSFVC9uHkRW1dpnhv1DoyiZ5aX1hFyhlkYpCWxKrs/OEYFAp6/eAxPYduQIAU9TUPscArzztP3YRKat+sbyzg4oLXTq3no1LiYqfQw5QuKP0pRpn2oROOQ2c3HhcL1S2QsrTRajv364e4AD26TYb5z1MckZ2qKS6+h1SJlVLcSKcfcSShEZ+pMvYhhfjajKklS+JzxEsexMw6NMc8UGC0N53q99oxrsjx4iqJwa6NQytVB01q7bxkzr4cVxrFjaRbwUAah6TNFDakOGq2TyWTyjPH0PInrLaxzHFTtvESfmavT0wGGTDzX6XURx/St4SzBhQ6NcyBq4NAXHPQT/OUtcldMQon2Zc5Y9SPssf6Y9ke4/Abpj6W1dQxO6Y9TPUYtZmbp0VOcHtFaaqh1qJTJXHMNzYzc/eNDSLWYTrWWiPwAqsfEiVDwwho0b7plmSDheKVEBpCskOoiheW6cz/++H0cHJNOPD3dgWTjR2mD0zG9P7cJBpYuq71WhN/6G38DAKCDBt57/0f8+xpevUlul6mIkAe8vqYTFGxseJ58rivkrHz+6S3H0nztxivYvk0uGxjMCWF9H4prSHkWyMcc95IVOD6h8yYTEg3WAZ4AZpzNK6VCjS38FREg5OKnM09BDOmZRyc5Rn2aN9+T0CesDzwPQcbxiQ92oFboPDu5/AR/+JjWxX/yO28+t48/+PMfuvX+8ccf41e//VUAwKXz6/B5f2RZ6rJ8B8dHqPGYtFotzNhd6NWajvrEWAufCU0hDPJiTt455bCJe+/9JZZWaC1f2jyPJmciWivQ5/4eHx1iMuJMtGkXgvXxBx99iuGA5vRr3/rqL+xX5dKqpJJKKqmkkkpeevlChGf7eIZHx5xVNH6ACy2yjn/tqzcQhGyVt0LkKVlqO0d76A8JzRA2QLNGN5mtrct4ZesKPe/5CDmLStiS8YICHDW7SMJmG2ZEln6tvYJanaC+5GSCLiMM7UbL1cXxjEYI9id4FoVd3I7zfR9lcocxOTKO9O8tt/DO1zkAtHWEB/fIukwmAwhT1vY6h6U1ghJX1nt450tkue/e+Rc42ntE/Q0SxJwFVvdyFByUd9IPwbQCiNFCkDPVugrQf0LBw7PpAOcuEDL2gz/55xCaoD4/FI5rZhFpNpuu9khmLVRRVq+VqJXBtUWKtTKYudGAVyOrud5s4YQJtCaDIS5w0PLpIMUspRtjXG8i4KC57cc7+PwRZSFEfg1TJtrbe/oUo1Maw9l4iBoHNnY6HedW00a7m7lSGooJAaPnddAagHkuYKVDAB48fIDHTEymtELEdZH80HdZBJ5V0GqO8JgSHrKYB0JDwoo52lOv0a3j4tUt9JbXeAwaEJzR4wcRhkNCXbQ2LrvK93wIprk3SsHzFkd4hGw4F0tufYyYJHCwf4zTPbrpX974Fnp8OzoZnLqg61wIHH5ApULeeONtXD5Hczg4PsWtR3Q73di6jJzdwoPBBFMek/c+vYePtzkjpb2Ec5dovXvdy8i5v8YKSMl1eoocKaOPWT6FMYsHZnueB2vKTBXjbtEW1rmyjJmjcEopt45qtfqzfDuM9kgp3ZyqMy4tY6xDn6IwdCV7vDPZW9YQwgkQsmfEHH0s12mj0XihoGXpx+6GrLIZUkZRTtMpukzmqgKJScF1kk4DPNwhnWpXzqGFsh5Sgs0Nel502pBdaufw9AAhuz5f27qBlWVChxKdYcIJGfW1Gq406ferMsDXOYh2+8kIHnO1vPrGO4ibtF/1/gCT0WIoliesQ/CjyMd0wrxQgQeP91OkUrQCQh4OTQhTkI5oSg/ao7F5cjp0JV/GGvD4pDjVAU4ZLZHIUG/Qt26eP48PfkSI873dPl67QWtc5DFOM9Zx2nIgPVCoApbhsij0EceLewUmaer4nN780ht4/OARACCbpY7MMlfKlRyZKumqq3sWiFAikQI5nzcNCGivrJzuI+a92JYSDXbJ5rAohjRWevchrvI56hkLu03eiInvo2A+p7oR6At6/v3v/wS6LLr3O8/v42effOoQm0f3H2LChH7rK0uu9EqapjC858bDkct086TEhPW+9gSShPmF8oxL2wB5lrr9ev/+I3z0ESX5pEpxJiZwbm0dOitJQI0b81Zrjvw/ebKD/+P3ie/o6OgU9+/RXPzO3/2Pf2G/vtDgOZzleDykDyZTAc2T0Oidw9WrK9whDx99TBHc7//8Z9C8oITUaHMdj8vnLqLl0yFRD4DIK+v3WKTc6Vwb+OxGaTbrGJfF2YRGi4nGrqzeQLvDKbLGImNofmZCKEeGJN0BtohEUQRTlHWdFKKwhNCAWp0U343Xl9BboTY8vHuAvW0m0DIRImbxbK9sYH2d3HBROMDDQ4p3EkEXAadjhtJA+LSJhQ0QCcoCW/aBliBoVogjnJ6QMvrk5+8BflmEMsUH71M8yo23JWrF4oflrVu30OHYp2az6VgqtcoBPoDTPHGHRJJatGrUBgGLRllLa3PTwfdGaEhm7vQjD+cucer92iqeHFJfTg4HYNJo9I/72H5A7rzjg313mF26fMnFQKyur8Fjf7vKcxg+nJ5XQtRaNa8gBwHL67TfP3KpjFEYQbCLVXgCJSWq8CQkSveHma9fISDdO6Uj//K8ABvMCH5+6xpaTH5J7pcypTqFz64rLX1HRpZnhVOCnidg9eJuSWE1PG6z9KSrRZarDIaNvdFkCo8hYN9GuL9Phvb3//LneMqw8q/tT/C1t94GANy9ex97BzRX76QFnt6nnx89fIyf3KJYrXt9g94WpcCvblxAo077vpBzN4AE3JoSJoWQdLBGUQpn7S0gSs/jbbI8hzhTwBRiHn9VGiRBEKDeoP4ms9m80LHnzY2iPHep97rIUUYJ+b4H4ZWuKzuPHSoUdFEaV4AKyhpbddSZGLXZbDq4nzJZFtc324+fYPMSZW22GnUcMEmc9S1WLlFsRDodY3OV1lg+OsFnDyibpQODEc97mI5x9TztjPrqBoZcCLUTSdQV7dfZyGDK9CF+GELWSR+vdNtoMht6cXKKQjOh23APVy4R0enS8hq8JvVreUViLBbso9XweJ9ZWOSlfk9mKEqXY6FRZ6Zw3xhIvnRFMsBRQi6SLC+QO1ZyC0y5zpS0uHaBdNNrVy7gqxyzJtQMv/dHfwoA6Och3rlGurUeB0jUvMZdwfEkcdwA8rLGnXL0BovIJEmdC2bw7o+R8vuF57nCoNJaF3dmISDZovaEQF7WrxMCEa+jmjVolES9CMDecUhrUSvfoy0suwvj0IfhdG+rtSPR1FZizFmhA6Vxn11OqAHLXDNtEbl4bhMNzsaajof49GOiJtl9/PgZV3BpExgzj3+0xmLMWZKpzvDKK+T2F8JShi6AWi1CyvW/HtzfdhlhMgyBNu3127cf4PYndFHTRuH1L9F7vv3tbzsdkCYpHnxIxpL0Inep/mVSubQqqaSSSiqppJKXXr4Q4bl++Rzu798DAGRC4GRIVvb23jG2mNp+lqb4nIO2klmKktGg02li6xLBis0ohse/r0cxLN9U8yQBX1iweW4DIQd8tTs9KJ8s6Nt3d3F+jW6VnYaPCQeaRXmBUcKWO0JwEVlM0hzKLo5+WKsdB4MnA0dPHoUhlKY2eEGBi5cJkegtreGTkH7/9MkpZEa3o3PtGP4V6u/lqxdw+9EPAVD5I83DnCoFwxTpFjkKLq9w6uVIA3JjmWKGlLMZ/s9//A9x6RUqmfC1b/0q/uSP/gEA4MGtU1y9sVj1YoAzVZj/RUrprGnjS4c4xM26gySttY7TYro/cXMHY3F6wvA6FNY3l7m/55Er5rTwPKxv0Np4ZetVXFgnpOu9n/8UP373XRpb38M7bxPKMBmOHERZi2JkfGsxxizsKtCYow3CWIiyoI02DmHyPd8RaRkxD6a1Yh6MCsAFuBJnhHO4QjK3z40br+Gtt4hgMq41kbBbL4rPuDYEEX0BgIoKV9l3OpmgxbxTymhk+QtUS0fqbndWxJCS5rPVjPHmNWpPo17Dv/g+8Q5FvsSTA84k2T1Cxgjo9977EHeY+O5wcIoGVwnf6K5gmNG6/uN3f4yhIjTjypv/BhobV7hfHhQnFnhI4YmSy8hCC0Zkbe7wDt/z4AeLu3tG0yEOOLNzNps5SB15AWvn7qqSS2d5edndKsfDY1fKBpjzKQHzm6cuUuiC99+ZgGelNbQpa775SCcDfo2BqPN6CGJXukLKOcok5YsFvHY6EaDp9judeJCM4F57bQs9Ri6QL6Pr089/8tP3UGtQmzuXFWbcBluvQbDbf3pyhCyi8Wkur+D9D0hnJ2OFNy+T/gjbHo7Zh64Kg2HAXEn1GGHMVdc7OTImXp0cBFgqfcnWzMlKnyPGAKosZ5AXKEreKaVcHTlrNATXVoymx9ABoXR7U+DuXWq7pxIYRvuGoxFubBCC/Ne++iYaXUKqVDpFl/udC4krFwghO7q7hwePKBj8fLiM9jrNf66U23Ox0SjYpeVLvFAtrSQvHGKTpCNXS8tq41zfEsK5b4y1zmUqRZmnBAAKHiuoQBgEHJZxqnMcM1LU9yWWOLyg4TcgGaPIfA8ee0RUFGDC+2xkgWEZUA9g4wqNyatf+RraxeL4xjvvvOr03/JSA19+gxJRamHk3H+j0Qg/fJeypR4/3kfOYxhFERT/rW9j9LisUb9/gibzu7391jv47DMKfSgKgxajSUpotNp0HkhrsMblKlSRO/06Ho/nSQlpAp3QWhqMpwie40L/QoPn6loPSzUapOHJGJInZO90gjykbKmD423cZr9ZnhXo8OH15rXruMrp5/W4hpAPAOv5mExpwxtr8PprnM3U7SAdjfj5GKdj6sRaXUBHpHx3d5+6wdAGSBhWrLe68NmXr0WGabY4POn5Ekafrb1Df5ukWbmOISAgJD3T7tZw+Rr1y9oUB7sEuQXqLdiy/InXAJjYzvPHMCgNCc8tWOn5LgulMBZjrgfie0t440tUDDIIBO7foZT24+N7UDkp4uFxisFysnAf11dXXAyPznP47PtVViDhaPo8z93BLCAwm3IU/CxBl91h1hjUmbzKD2q4cJHG4bvf+QYEOG5jmrmNODwZYXmDFnun28bmOj3fbjccSV8YhmiymxICiMt6SHmOEa+H50liJWJBm1DCgrM4sbK0hIjjZKS1MOXBp7UjYhRCOIj2Xz28pD9n/fWZXXt99TyaTSYIsx5OuYZNnhZY4fiZOK6hZEYwUmEwoDRRZTSWOU4jEAFm6eLpzLm2kGXbdIGY1+b18xdw8Rwpi4N+jlt71OaT0R4mpxR7E4Yhwe0A9vaOscuuLoQKl8oijlEdDx6QS2v74CmuvE7ZPcsbl5B4tP+UmqFeskMbiYwNJAEDw9C2lBE8lD7+GSZMGLeItNs9RFxoVWvl1maW5u5AyvPcGRthGLrLSlEUUHaevVU+o7WGYnebssYRCcIKxDUat4YfIGDSwprv4dH4hNufIbClC8x3a0NrPTewhXghg+eVrYvocxr4P/2Df4ZWndb78rffgLKlEdvBdEhjG7V7uHKFLnwbS5tQfNhMs1OMZ/SeYjDAhS/TfJl6jJDrnQl/nhVj8hya3UU+JBTXarKtAE1mQ77qb+L4Ibma7nzUx/mc2rZ7cIrdJ4vpG2WMI4pNphqmKN1JBQS3RUoDZPSdRjHBCRuYk0Ii4AtPzSeSTwCQMNi6SC6+c0s9pOy+UyqHMWUApsIFZt6/dzjFYEx7a1NrGJ7/Qmvn/jVGuRp38L1n6A6eJ3/tN38TAR8Ok9EYH71PJIQnh8dMW8FnBh/60lpnMHqB7+LCap5wBH2+L1wdv1YQo8NhHMudpiP6i2s1CH7GDyOAx7OQPiKOW20phbXyciAl1jjmsr1+Dk25OBDg+8q54mu1JcgyXkwBcY2pUk5jNHj9+rEPwXq/3qjBHPK+kQGOeL0XReEMlYPDfciyIoKR2NtllmabYPeAjNVQeAg5wFZ6EjtPKR6z1+vNa+JJ4KtvEgFjmqQuBf6XSeXSqqSSSiqppJJKXnr5QoRnNlPw+KqqDWDYQvzRR/egPSI9Gp/s4DFX57XW4vwmWeKX1s+hGZZkfdLdeAeTMRTDym++9Rq++2vfBUDVdPe5votKUlzsMefISYDRlCzWzfV1+Mz543k+6mztaqXI9ARlX7xI1kQyS+GxdSzOuEaKXCFmki+tDQRnTQSBj+WVMnhxGUlBgbi5GqLbI6RiNB4615g1GiWeKQB3S4/qLayeJz6EtaUm3v8pWcEmNzh3kTJhrl3fxP/2j4nM8LOP/sJR/Fshkb4AijUZjRyaNDg9cS6tIAydm8SXHoZDQtUmkwkiDiSsx7ELBvUD392KDw9OsMw1TzrtBiyP4d7ufYSSg417HURMnLi0sowek/G9+dZrmEzINTkcDt1Na31zcw6XDkb47PPPAABvPKd/P/3sIVbWKFtqfbWL88vUxldvvoYOk3xNZlMXmKqMdjHOUnrOVQHM3R/GGNdvCIFWi1CuIKg5bpZareaQn9FojP096sdSbwk1vgUdHR260gbNZgvHxzTPK+traDbLjJvni5XxGU4bBZ8zcXQh8ZfvU/06G1/E0hXinwjyIYJ9Qh9nJ0fOtRfUaxAhfVeKFKsbtBaO0xynBZeOqa3OM848gUZM323Uu+hw5uVoOsbxMfODTMYu+xBGoby4WVhYuXjQspQ+wnBO6FcmXRmNX4ioRFE0D2AOQxRc1yzNMhcIn+e5czmIIHT7IAgCWNZnIogc944nrNsfShXulttsNX9hvaUXraV1cnyIT35Ge7pmgdevk6vgyYN9tHuEbo3MEP1DQjdP0xmOmStpPVuDYN356UefYbxHiE0jbqB9kfo78Ye4dPUKAGB3+wB7fCtutEOHZLfjGHtMIrt2edPRKwahgMflRWTUQsRkq8Oj+zh6Olmof0pppOwJmE5z4v0CuXsiRgeNVi5bKhAaLUYq6vUmejX6/r2dbZz2qU/1UKLBWcGnw1NE9bLivZjzvZgcMSdRaJVjzMHasBY5I05poZCySyvQhcvY00I4d8wi8t1f/zUkYxr7jz/8EJ0u6f1WM3aubGsM0pKQUmvUyuzibhddDn5v1yOH6nih73RGq9FEt1smmTQcp5eVQFa6YY1AXtadyxQsZ7TJokBYuluFQMT1/aTWyF+ggMbO010sLxOqE4YhYt4rRZ6h4DHMZxMsd6kv28EBBuyy7C11XXZmvVbDdERzoQuLnN0gB7uH2GQCw+FgjJTRbhkARcJOP89H1KDxyZWi+BAAT/cOXYb2669ew1/59a+BBx3mOermCw2e9+/sYLdPCz0XkSN/GiqJH/6cDiM9forhiJ5ZW1vD1hbBS81myymC2WyMhBddXKvh1deIufcb3/waekz05/VaaMc0qHuPn8BjODNNpi4NMbMepmm5MAUCjsk4Gkww4dT4XAt4rLAWEWvhUryjKIBXxnnAImTWrDRLMRywcmk0EcVlrEaGzJDiuHv/x1Ac9b/95BNIjkGyFhAeLTqjAcWxAtnAoLdKk3zt2hu49T6zaaYpfvLTPwAAfPAR0Odsr3rQcnEMUSNCWF88jVIbjZRdC3meu5iZSGvnKiiK4hm20fDMwVCvkyIeDAYYcBzA0dEBLnDK/Gm/77ITBCxq/Hyj0UCTMwO2tq5if59cLJcun3cbYjgcOuPH9323yeJajFdeeWWh/v3zn3yEJtdaury5jC/fuMDfvIlf/Ra5B//kz/8MpqwnpgxKc9HzJM5ug2cOL/652WjjK1+mOJnXXn0Dq+sUT1BvNiHY+Gk2l8/EjeQYjalPg2EfDVZwvaUOxqwUjo8P3eG7iEjrz1PvAWxdp312YSnEx7do3fUnHiwbKvXmKoKrXwEAJKsDF4cVxU3IkNrTEAYrIRlgt58co7lBpmXrxIPHacNrqz2cu0rjGYgAD2+T20vlQ0QBx4U1PJywsVEUOXyvLLqr0VyQPBIArBFzdmNrnBEthHzmElMaJFprZOWBai1GfAj1+31knE3oe57TB9JKlAmcxgpXc83YHDnHYtV94dLYhRDOGG40Gg5GfybmS4gXMngGJ8cYj8mF85WvvI13vkxz9OHHH0Am1J5ZmuL4mNygF69uuWzRx493MGUS1mQ6gx8xW3IO3P6EMrmGQYqt1+idWZpinWMgIOfs7NPRCBnX3mqEF6A5rlD6cGTenZU1RBwfc+niJdSDxQxXow1m7EJKfI2Q95znBS6Tr8gTl86s9dwdqpKJy/Bqt3soBMf5SB9NPg9a9QBSljXxCmZZB2As/DJLT3rPxG2VddUmyqDISneYdlmAWhdIksVDBPJCubTrIPTx6mukp3w5Z1oeT8Y4PqB4NKsK1NlF1e100OSwgCjyncvR932XRWojCVlnwynyXMxPoRVyNlrzQjsDwBjl4p2KInfuemMlcs6SjI3CC7C14O7dHTx4QGdPFMdY6ZDO2FxeQo2NtHoU4/oWZRzu9wcIyrR6YR3RcLPRdlnTeT7P6hLSIuXMO1Ok7lJVGO0uH4WaZ8/VanVMOOP28c4OShBhdXUZ//JHfIGII0dT8cukcmlVUkkllVRSSSUvvXwhwvOXt7cxY/MyCGqAX2YseLDsQkoz5Xhatq5uuaDWQZpD52Q1h4FFj7NTbly/gZuvUg0e3xfI+JlGvY72MgXnSeGhxTW8tPCxxNlMk1mG8ZR5VQIfM87jj6O+o9o3qQHM4u6eJMlhmDOlSAvU2WUjPYnphL6lCgNbBhtLHzO27o2Zk9l9cvvPMOjTLXd5XSPj6sKzaeZqFGlpHQ+E1RMcbFPW0vcmt5HMmLcHEoMjyoSC1BAl/4hfQ1ind65uttFZqi/cx+FwiFardD+14TE0b84gPL7vo8G3kCiK0G7TfFlj0D+mbJ/t7W1MJtTH0XCI9us8j9488PfSxU202VWTJKmDGOO4htPTAY/bPOiz2+06/p/pdOpu77VaDZ3OYrwR+xONmLknpukBZlx359/6138Ff/u3/xY9c3iAW3coKyAIQuiyhhv3EaBsivK27vs+LNecunhhC69cf53bu+yCsvNiAsXEYQRlc/XqLDuDWkk0GM5WOofkDLJC53gB7kjACAQ8No3Ad8H+X37rdbzyJqFPf/6j9xA3CSF7vNfH/oDns73hAiiNDJEbhtF1gomhfXMytahz8PjS5uu4uUUo1ttvvoWtm0x++b138ZCrOJvQuL60m0tYXSbE0Vrj4ObpbEZ8JwuKNuoMWjJ/P92aS74PA8k388nwBPpMMPOEUSZl51l4ymhoRns8CIArxWtYFKJ0O/sAu29mmUHOmWjSCxAyrxGk57LPpJxXTqf/X3wiu50W3vr6O/zKFk7TU26bRsalHx4/3keZULO5vAHEnM2Xz6C4RMj6Roj1ZUIWhPbQ75P7Er6PgtHTXq+HkPf6uc11GA6Q/fBnP0FjifVcfX4EGDNDYelb03SIaEK67dLWdfjh6UL90xCYlrpPFaixziqKHJN0jq6AUSVtPKSKdaJnEMiS6NHDAZdI8JXvguV9X8+zAKXnxl6pwtUvDLwQvsfvLGYoGP2wIkJQEgNaHxknckQeoOXiYRDWWvfdMArhS9KtgZQuZMEPAviyrPo9cZ6DMIocd1RcCx0aE4ShyxCNazWEHFIQRpE7P4wUEOwSsmLOHaWUgXZB+gaG/fW+7zk3Zpqmzj27iOSZheI5Gg5SjI5Jd8degPoGhQ9IKbDJCOJv/savQ6m5t6BEQ2d5jgPOFk2SHDmHYhTFHFVL0wwpr400yzDj32utUeRlHbwEgnV8sx660If+0SFSrkfXarbcGfbL5AsNnnGm4POBG3tizl4qPBT5HAI8t0nK0fdD9E/IUEnzwsFU33jjVdy4cgkAsNJZckaCUjkKVo6p0o5VMYMPVfoMjXAFAqEVeg06PBq1EJqJDWv1BuIGTcj+YOayYhYRTwbwmYBMFwWGQ85kCPwy4J5rfvDiTTMUnIIdBIGLNNfoY2mdFuk3l67j6RPaBHtPJzg8IANpNk0xm9IiCoyBLejwON7bgR+yL1o24DNrrfQNlGTXmD9CZ5X8uu1eBC9YnMFWK+0OYKWUc1GtrKxgwHE7s9nMbYjxeOyMAAGBO3eoAJ3nec6F4MFiiWFOAYMZw8Z7O3tY5w1RrzfQblOW1vnz5/Dpp0QQZYxFwMq33mhgk9fPeDx2mVnD4dC1Z+s5/RNh7UzhWYUZM/3OJiO8/SYZKn/rt38L//B3/2cAwL3tRyiYDFJDAHwoyDMGTxhGEPzObrOLHrsBl7t1rHLmGYTCjIv8jUZjnPTn2T1lPaF6awWWvzUbJ0jL+jFCwm8t7nrtdpoAK/Rza22cO0eKJm7V0UrJILn+nu7k+QAAIABJREFUyk2c58y5w//rRzg5KgkAI9jSMNAJZFk4Ug3QYJsyCtt4+oRi6JbXl7G8Qn0sMoMPfk6G4sPH+1jiGD0rPSRsYGSFwSyh/tbiAFFcxny1nkkPf56QgTOvieYz26MxxtXcE0K4rBuVp0wmCKRp7pRsEIawyM/8LafPqxyGjZzCzi9GRgdA5IK65u/xfUcelxcKMR+6VHx2XtvrF8X2/DI5OjhAa50udsoP8Hj/Ef08GWN2ROMZ2Da+8jWibahHASzHtaycP4fbJ3zZyqZYP0/7rFvv4clDLgDqpRjq0pjXGPN+0itdqJLUUeXY4ExTFQrknAypjQaY7uBkfIiMCVk7vRYyu1i2XWENWpxZE4cWCdOIpGmKVpv0WloYmLKem4ygmNIAxsLnuUqzBA8P6aA8v9RE0CTdmmVTVwQ4M7lzgQrMwwWklIj4kpFr64gkEURQvL5mClC6ZDxW0C9wSdZau6wuIaS7QIaBD59jeKJazPGbQC0OnS5rtdvuUA5C37lMgyBwRks9rjngQErP6SQP0lFkGgvHpG4gYEom5yB0Z5Lv+e4immXZM0zkzxNrAudqNMY4guCDo1N02EXfagQQTE3RCAVUaTSGnkvJD2KBMCBdNRkn7gJs7ZwKwBjrwhOtJZLd8vcpnzeT2QxsF0NK4ShGfD84A0bI59InVC6tSiqppJJKKqnkpZcvvpoICZ8jxAWMq4VidQHF1llY70Gze+toJiDKwlTwsdqkG+yVi+fRZG4IKa2D8cK47nhpJsMxTvuEzGTT1NV7UkaCgR9YGyDimk2eH7rsgnbDx+Y682g0ZtjT8+Db50kYNBzFf5bmKCl5grgBzTdqZSU89j9kqULAtWLCKICjqg+Ced0gobGyTihKb6mD8xcIBTg6OkWfXW+jUYYs4fGED81ogueH7sYAmaPG31pe6+HcBbKUpRCYzBbvYxAE8M8EepZwoJDSBQnX6nUXSHhwcIC7d4lMstVsOgh2qbeE7W0KWo2j0GXjHB8eIOR5aTXrLhdgOpm6oMIgCDDlNm9vb+MSk1JOJhMHWx4dHWHMgafWWrT5JvE8kVY5dv96LFHjcgDSJhCC3v3d73wdNb4l/vGf/Cl+9iHxG+0dnjiU0VqLnG8OQRA4hKFTj7B1kW7lr71+DefO0zx4gXTtPdjbx8kJlz3JFCyPgpU1JHxTnsxSxxGVKYPhydFC/QOAc5trMIZu97/63S9j6zohLdpaFzDebIR4eJ+qww+Pd9GMSvh4iiZnyxl4aHRpHDqNdUTsIh6cTODzPjg93Me9kMtVDDUMIx6JDaAYUg88H5aDkyezMZKsJHcrILl+j7X6hQJ6pZzXsZJSwufvGn9e4yzwpENmrB+4W7SU0hEVnv2mUmpOeX+mjpwx5hlXlAUhGGfv+WcDkrMsheJ1GoaBQ3XOVnhfRPYeHaDRoWzF9kqI7QPSefneFJ067Ym3vvw21q8RIn4yPsSTHZrTk6cH+OEPfgYAuP7KFaSG2jyZHCAOOdMJIUbMcxbWLbp1QuoeP36K0YgQE68RobtGCE//eID+ET1fbxgEHKhcb3mw7H7/7KM7mE4Wq6VlVIqA0WcP1vF/5YWG4PVihA9T1hOzHiyjR9IUKNMJm60uyghqIyNkgtbs0aCgglQABpPclYKZZRn2uQr59skMYsSuOfkUl3uUCefJGGMmG9Qa7uwx1iDPFg9apgxCDrpWyiFnURRA8nmZ57njhYri2KHqQkqUFSSt50OXfm0L54qC9ODSmoXn0ERt4XRVoY3TMcrM0R5tBcrESHGGUFGfCV9YRIwxbu6EEK49h0dDtNvH3MwuPD7/QuHN96KQju9ICI0lzmJb6S252mGeJ57hQivPPF9Idw4ZO+ce0xbOBZnnOTyvPFc8jJOBe8/z9uIXGjxhVHMwG4SELYs8qoyzWwDhd5G5I84giBmmLzJEHOsQ+9JNeKvRgl/CeGHsCsSdHPUxHnAdDG3QZrK7WWwxZlbOWlxHf0oLsx3UkHEq2/7hEXZPp/z+OlY7zy036eT4+BRlTTWrC0jelPVGExmn2iZ54gZeCg+eN/+5TD8vcouAJ02pOXxYa8SoNagvjXaI1XXyIUynGvsHhCUnsxxTPnikBAIme2y2u+j2yoynAMIryd08BP7i7pD3Pn6A3/53/20AQOADOUPeaZIg5My7dq+L8aisY5M5QqmiyJ17Q8HDAbvAtjbPYblH/cqLeWS8tfPUVlMUGHGtoFkOnHC23aNHu7i6RY6qdreNZEa/Pz4+RZtdlmEYQpjFDkuttDNIo8BDGJaxGRkMb/I4DPDNr1P64vmLF/Gt2+Smu33vPnY5Rff09NQduM1mE+tcGPStL30JX/8GUQisrizB4/TXNJ3BZxfAlcsb2OJsJq2sg26NEMj4wB1OEnzOJJ13HzzCLqcMLyJ/5de/jmvXqT1rKw0cMnng7tM+9vnnO3c+dUzFwgvQrvF38xHyhNZaEMUwnPaZGYPx6R73ZYiYjevdw6fwBceu+OsIO/RdCx/JjA/TWoqCSeUKNYTw2KhAgJxj6/JiAm0WJx4kxer+z8HTUkpXYNZCuxTis4VJgyBwht/Z4p5Zlp2J81DuQKW/5zgPAAW307MWQUls2Ahd3BS5sax7Z+lmD6MQL2DTwbMB0hEzJ/csljtkSPePLG7cpMy7jQs99FZo/I8HBUacmVWrhwi5HlbgN/H0KWU9toxF5FN7tg+furpa569cQsJp7I93d3GOmdEvXj0HrbgQY20JS2w8j2bbMGzQdjsNBFyE0lMWvfZincyVwYwzj04ROhdi7isccBHPdGCRMIv9ySSZX9506rKodk5mMJwBedCf4Pd+QLWckqJAwuzN48nUudjzWQo+SpBZDxn3e/t0hPYNinHrhU2Mx6zTpznyIf1t29fw6ou7XrWexxGFYegu8/V67AxhpRWaLZqrOJgXJ7UWCPnnII6IhRlEtFmeGbmKkOWlcTIn2kzyAin/vlDGxWqlWYHCFcU1kLbcK8qxj0spXyjWTAjjQjqEBMpigspYnHLYR7cTocnZwiKIEXglnYNCFPKYBMLpQilCREHJ5pu7OntSOi5/NOLIEcTmxbz2YKGt0wGRHzq6EyEs6lGXf4Yz0n6ZVC6tSiqppJJKKqnkpZcvRHiElC5y3Pd9Z2EVRTq/ffmRIwgT0sAy9JUXheOfWVtfw+YFuv3WanWXi59riylTgGdZ5qzg0A+QsKV25+Ej3H1Ct9BGq4t+nzKY4nqMdEpQltYKe32yOq9dOo/rG4u5QgBgPJqiy2UOhKWAPoCC7Mro/jAIHbweByGUpkC86TSFAFf69QDNmQGFUgjYxTNLEheA1uo14DGctLweI2BgJE1yzKYlGWANIXMJhHGAXo/+VpsMR9x32ABxvDiK9Y/+4I+xwSjNr3zjLaRcsTaKai5AczyZImUCwFq9ji67BwanA8Q1Gs/9/ggH+wTB/9XvfgfLq1w5u1DuRq20crdKnQlIvvmP8gLHTEC1MsnRYEJAYzOMmbMmz3LsM8nayvKSy9h6nuSZRcSoTlHkriLvwcEuNlYJnWh2e/AYbr504SLOccXzb3/zG5iyCyBNM3eLD8IALb6hdTqtM9wvBlMOAC+KDHUmxoriaF6x3Ur3s1Ipcs5OO9rfxsHuAwDA8cEOdrYfLNQ/APjg/Y/w/gf03dksgS+5SvhkjN09Cn6fzsYIQ/q90QLTMbnMtMldpkdRSKTMITcxCtLSGpeiQJbwrVJlyKa01tLiCI2I1o7UFgbMe5NnUIwUqezEcYhoVXOop+dLQC9OdiaEcG4GIeAIA7WYJ0xkaYYRo4zCKMfbIrzA8ar4ZzLmaDrP/FzOrx9Asq6SQkLw2kinM0wntAY9KVHnMiKtbg1FMQ+ELnVDeoaccBE5d+4Szq0S4VoQhpgw2p0Zg0TROhxNn2K6Q1T76XiGa4yGtlZ7uHCVMiMffXIb0wEnTwgfKfPk9NbOI2b3XL3dcs+ErQiW++jJBrySr2ml5jLdPr93jKVlui1HXgsTjiVodOrQw8VcPjsjjceMOH5/cADN5X/yLHc8WF4UomBXyDjLXLVxred7N1UFkoxdOUZgb0i6w9r5WrbaAuzu8TwBXZZXiCUuMKldUKtjyG0Iw4lzfR9OND68RfULY2Tocmbp31+gj0prh8w0Wi3nTvR9z+2DqFZzrijfk5CM/Hie5zIItdYOHVRaz70FWmOOIcGhN9pYWA7eL5R2QbxZXhDBJosLordAwU5aT3oQ4sUSCMqahL4n3V70Pem+NU0ydDibNwhjSK/sl3JByErl5RTBwDh+PaUK5ExgSFxE3K80QZ3PP3MG9cryAtrOXWAJu9/DIIRkM0YVhbMtfpl8ocFjjIHnMrPkvLhkGAFM/iSshCrKrCXp3BNGZWi2OD6k2YDPB3RUb5RhL1DjqUtPbrfb0OxfPe2f4uEOwbUPH25jxIp1mBscMCGXMQUizsTYXFuB75dp4EOMh4tH3NdrTUdu5Au4dOw0TZFmJXNke65YrYUoyQm1B8mRRMoalwIYRfMCa2k+Ru5YmuH8z3kxhefTd5vtAOsbZDzkuXKF14QUGE2ov74nXQZAkZsXio24v3uIn/6c6r28efOKcw8HXgAjylTqFBmTQ4Z+AJOXvw/x+ICU788+/AQcQA/pR24uhJSOqDDJUoz4wBgNBzjilMSHTw6Qcjq/H0iXjdXuNLC2zi4TA4wHnP4Yx87IfJ4YA7cx+ienWHF20iqGbGyMpzM0u3Rwh/WGq+vTbtbR65TMw/PD2VjAcAaCUhmGTLiYprkjxqo36gjDMh00hy0NHiPc8w/v38X9B/cBAPtHfUeQOej3MewfL9Q/APiz7/0Ipf0Xx03cfOVLAICV5RpUQQfl4WHqst9OjvuYjbkgrcncxcXzPQTstg28CD5vRk8YFFzHyDc5dMZFPE/vwfNoXfjCm9cjswHShOORiqEznGFylLSO2iiXXruIBEJCs9FCfn02VLR1rLih52OZ3d3pbIKC16wLKKO/cHQOQQB3SAQicPvmLGOzFNIVMa41YxhWxAf7xwjBF5HCQPEhJAMPcZn+KoAsXTyebjJJ0WIIHoHB7iHpue7qiosNTNIxDpm0rtFZR7PNsXt+iP0jYna/f/8u1pbpUG82lxDxgX3+6iYeHdAzp9MRYob+r71xzWW/ZEmO9d4aj5vCgzsUr6dNAC9iOop8rtfTPEOWjRfq32Cc4Hif9MWje/ccq77W2h36fhy6GlLWhwuVMBaOXNVYA8WEdcJYROwmj4MYtfJiHPkIObsubtZdjbhA+LiydYXarulCCQAjb+r0hPBCWA6/OBlPcfC0v1D/ADp8C1XuGzvf91a4OBwr5VyPa+NqMcLzIMsaW0q7s4QuSCXprUBSFjb1fWhHsWChdFnw1kCZ0pAXc2Z/a1FGolljUTJtBsE8ZmYRsQauykIYRojL2FkpqBYagDQ10Hq+7+ZzXZwhDs7cuSLP0AhYKBcaYgzmlwYBl0lnpOeyXafpXL+GQTC/YOnMFcbWWiN6ziW5cmlVUkkllVRSSSUvvYgXQQoqqaSSSiqppJJK/v8oFcJTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89FIZPJVUUkkllVRSyUsvlcFTSSWVVFJJJZW89OJ/0T/+xm/9pxbCAgAkQnhsH/meBCAAANZaKKUBAEIIWEvPe54H36fXZ1kChRwAYIQHy5812QR+GAIAcgN0eisAgNHpKUL+WykFjDH0fisgBX1X6wKFSunnIgWKCQBAaYWiKAAAP//+PxHPG4B3/9evWT+S/C0PxlBftAFOBzMAwGwqUIsCAECjK6CUAgDUI41RRu2/c38KIek9m2s+OvUetScPoHJqp9ExjInpZ1uH8JsAgIPjCYxPv4fMoaZDAMDxcYHO5joA4MbNdxCoHQDAzt42pjmNz9/5L/735/bxw+3PrCqoX0VhYY3l8bTQmsYKwiDLqJ1K5ZCSXiukRJ5l/CYBz6Nx8P0QludFGwMpPXpCAErT88YoCJ5rz4+h6XEYYyB5rASAQs3fU46t53kIeW38m7/ynS/s43/2d/59G/r0/TjwEXJbanHdPSMEwF2CEBZBQP0IAt990w9DBPzNoiiQcb/rUR1GW/eucj1CAoo7Zc+03WgFz6ePKa0B3hNKKShr3HvK5//z//p/fO4c/r3/7r+yPvdxMBwiVzRvp+MTwCvHT6HuRQCARtxCntGcN2oNgPdokqUYjUYAgMgLEMU16uNqByKgdlqtYXl/Z1ohS2kc8iRBUKNxi6MYlsdECg+WuoL+0RE6vQ63bQBIatvv/Q//6Ll9/I/+3ndsqVeUUogj2hNFkp5ZFz7CkNqgrUJucvd7wbpBFcq1Jw5bgKW1lhcppEfjEMe+m98g8MspgoVAVvAc2QB+QGOeZVN020v0LT/EybBPf+tJRPzMf/9ffv/5ffx3vmkfbh/Te6IAq902AKDbjlFr0Vw82e1jeWkDAPDG69chBOsn4aPfPwUAPN55giIj/bS+3MXqUhcA8HB7D8MR7eMkmTnd/OW3XsXWZWp/4M/V/s7OUwhL/1+vS7Ta1IY01UhmND4rqyvY398DAPyH/+0X65vUuCMAQgAePy1h6BcsotxO1uL/SzEC0L+wxQKG/8HMtyQaAZ47h3/3d/+JLXWfFQKW54e6wrpVCLcen/mqEMAzn5j33+lEISDO6Iln3lPqMAiIUqEZ615jrXFvtBBuH1tr3Vf/m7/5m8/t41e+8o6djmmthaGPtY1zAICD3SOoyRgA8Oq1qxge0Fq2voeb3/omAGDmS2QB6Rhtp1jq0d8OJwe4cvUNAMDa2gU83btF7+w/xNb6TQDA7/wHfx+9OukPzf8BwMHxCcDj/HjnKa5evggA6HXbbglpCyj+uef94nn8QoPn3sc/ge/TR+KojVqzQS/WGnGNNkYQBAgDUrJSBm7SYCRCj54XvnWLzggLyxOY5AahVxpRQI0Vd+JLdzpZIQC3EKSbfE9aiIAUog09CE1dsebMjltAsswiL2gC6826G9Qsz9Cok2KtRRLl2dkfKoSs4EI/wOiElIvnx8gTmp7Q8xB4pCz2RlM0fRoraSSSlBZRVIvQbJNRNEoMrEeT3O12cPf2XwAAWj2JGo9tFAhMJ/S3WaoQR42F+6iURlEo7m+ONEkAwBksAOB5whl7UkpnvHnSQxjON035J0VRPLMRywMpCkP4fsDPGPhsIBl7xlA487xSCpoNsCiKnZEjhHAG8/MkCALI0jCXAp7n8b9Y9z7qC7XX9+fPeJ73zM9gRWOUQsEHYtjsQPAY5Hk+fz7woVm9GGOgSyWoNaym/knuJwCEUYSsoANaa/1C6zTXCtOM5k1ZDWPJ4KnXa+5USdMUgz4rmo5Eo0UGtQks/IDG0jcSvSVaayLwkVua86PxMQSPoSg0PMnzEAVOmYZBCJ4qnAwHtF8ACKFQ82gvLtfbaIV0iJsacDI+XLiP01kC3+N2+j6mUzrQIz+AMdTfokidca1MASP4MgSLRr1F42AK1CPq442tN1GL+WJxuIsht2f38A4gaX6TVMPn9odxw+2VwA+g2aAdTybwBK/lwkDxPPr1EOq5x8eZPk4yp1MvXFxHMiLds7q6gbSg+Y2jOh48uM/9GuHihWsAgOOjCY6PBgAAa1MUOT0vVlbw8a07AIDJaIyrV64CALIsxsEB9bfTqKFdo/GZzWYI2eBfai9hNOULCiSsYCPTTAAe5yiqQ8pgof75EjDlhUrMXQgSwo3lL5OzF+b/p3J2SoQzwATEmX8pdYaRZ22x50+m0spdGi0AweN0tu1nDZ6zevKX9c5aC3PmeYszY3WmSWcvirx1IUFgAEB66BmL84zB8yKSZQVanTUAgFJTJBmt097aEk75zH6ws4urG/TMydE+dj75kJ559SpORmQg1+oGSRpyG1K0GrS+rl+9iTv36JwbD09x8Stb9HxUdzpGW+OMyTRJ0T+htb/U7SCu05maZAUU99FY68YBtV98dlQurUoqqaSSSiqp5KWXL7xCT/o77tYfBg1EbbopDUbDudEpBFZXCH5NZrm73QeBj3abYNZWs4VpQm4aLwwRRPSeIIxRZFMAQBwFCCzdWALfwsq5tVve6KQQzlq3VqJg/Ep6PjTD9xD2hWBSzy/gMUSeJEUJJsHzPPBFD1mRoRmRxZ2nEllGP0sbQRd8m4GFZXhdZRKJoJ9rvgQku/DgodGgviOsI1PU31ZDojDUhtkkQ7e7xmMFlNfHUX8HOmNkKdzAeDRYuI+zaeIQlaLIkZU3Q2vh++xSzOc3CimBoHQdWuNuZnS7oP6m6dzNYK11t2IpPQRBaUdbGJPyd+HQjTAMUWOE8KzrSnqeQ4HIVTpHR75IPE8iYgTD+1duVqV786yL1fO8Z+Dj8pvGGNcu31PQ/P00S9BoEKIW1yK39gulkCeEQsgggOSbj5WeGydrrbtd5UU+d7sotXD/AKDTbrvxy1UOWLopJVkG5ZAzD9EyjWVcr0GG1N9plqDIaBxsWiCI6Bk/kBiy+zSZpcgn9H4kGvUa7/Xp1KEBF8+fR71R534JJCk9H8eRG8NaWIOnaRzOr11Au9tauI+qKBAGvBbk/C4mADSb1J48z1G69rQCojDiv7WYTGhdd1rLuHnpGwCAb3/1N9BoUBuUznH7Ht1C//j7fYxmu+6dQa3N3wrQ6fDYzhSSGb2z2WxCG0JC4sBHyPMbBhbWL4H358ulS5dwid2OByd7Tl9qZbG3x+jTwSm6XXI/5VnuXJCDwQiK95knNHptQrF2dw8xmdJcLC2tYGV5GQBwdHzsUNzjfh+tBn2r3z92+yIIQ2QFrclI1xDXaWzTXONgl8an3epALHg39jF3Q8CSXgTINf7o4UMAQBzHuHDhAoBnUd+zc/7/lghrXRsAnPl5/pN8AYQOALS1ZxAYAWG+GLl6USG0h3/G3F0lMEflLch9BRB6VoZ62LMYkl0Er/rFMksSKHZZN9s159Y+6j/G+ga5kwrpYbd/AgC4fO48nj5+BACIum1sXKYzbJA/xeEhoZUGBienTwAAn33+c+zvklt4eekGti6/Q33xAvCRigLz9qdKwbB+XV1ZhSrXr1KY8YrTXoAmSnT/F5s2X2jwhJ4t3d+QQiOQHJ8D5QYbVsDj+Jxk0n9mAc/YBVP0VvD4KS32sBYBIKV2/eabONx7SoMUeGizMh0VFoYh1CiKEPBhVo/r7sDwAzk/WI11cLzvedBmcQUUhgYZw2C5MghZAaWpwiy37vfeKSmpbgeYpfT+k3GGJh+QeRGjQzoTy8sxJmN6ZzvyMUoYFk9mCNlVcHFzA4InJzUK8OmZ7adP4HM8T7u+gb39j6hfMkYYUDzPuUtbuHX77sJ9lJ4PlZLiLlQBjyF1D3LuQoJ0UKLRCnNUVMBjNwPFvZTGxFw5KTV/vsjn7hYhrIufMCZHmpLxE0WRO8A8z3smtqcUrfXCBoE80xYhgDobJ770nLHh+z7m+tQ6l50xxq2pOsezAEAtijFm19V0Okavx24aY6E1zb/JM9z79FMAQKPbwbkrF8tWOMXkeZ6LFaFPn1W+i6uj9XYXx1N2aU4TSDai41jCZ4MkSzPkUcbflZjxYZ2MJ+6zvV7XxXWYTKHJhnYjipD7bKR1JAKOWQumNSi+TFgN9Nhgl9Yg5/gvow1idr1aaTHTNM9NBEg5LmwRieKw9ChjmkyR83oZ5xnq7K4SwkPB4x9FIZIZxe75fgCJct+cx2s3fwUAsNy9gKVundup8HRnGwCgjYeC/exhHEPzAaZUhoj919aQ6xkA4oaB5li8SHpIee60ESiSxQ1XoMBo+H+z92bNlh3nldjKzD2e+dx5qHlEYSTAQRxEiqJISe6WWmp3h1sK+9nDf/CL3xwO/wA/uMMRbnd3yN1Sa2qrrXZLJiVRnAAQBFBAoeaqO09nPmdPmemH79t5zgXAwqkIPzFuPpAXt87dZ2fu3JlfrvWt9dF6mSYW7SodCjsnHRfshUEMKej5am2xu7NPYzJKUa3Q3A6FD53R927v7KPOAZIX1bF/QgHS7v4xgirN262DI1RbNL/bKysYDUfc3wKTMf2cFR56/T0ak1ijws966+k2ojCcu4cuyMFsvpvEk4eP6L52d/F7v//7AIgWNv8/BwyfuJ9PPf/an/P2zUFpmSkdLYU81d/p1SnYAj6Wg/OJrxPT/5vGYjP/cSr16VRAM72ucGkis/8uP9bv5yG18jyD5kAi7WWo89rYrjexu0V79tLSoqPh9nsjLKxREDs8GGJhdRMA4Ikl1Pn9qzaraDcvAADq1QX8ylf/EQCgVqsj5b1297jj8kczazAa0oHysNNBzAeXw94AnW16j5/+x7/GOKBF49pvfAcVpmdXv/yFT+3XGaV11s7aWTtrZ+2snbVf+PZMhEd4PnSZnKe86enUWsgyVhKA5ZOep/RMEqGARJlYGUAJPlXmE3zta18GAKi4hScPKNlOGQ+dMcHro8JgOKFIbTb5q9FouoRbIQ0aTLFleQ6fkw6jKHouaDTNFTI+8aoAmPCBdJwAQ6aQ1Ez4HQZAntOwFbnGIE95SAJU+WTY7YwxGNKJSCFDrssTuEKPqaP8/mO0qoxc+QZWc8KiyuFLGre9JyfILd3DQElkQ/r9QO/h+pUX5u5jMkmnqITVsPyzxQydowEpp1THFOadfkZrc+q0MksRgVGyKFSwjPBok0MzJRdGVdQZAvN9/xS1U8bds4l1UsqLGV70AAAgAElEQVS5T35SSRinDvQdvCuFRBAF7l6LguayNYVLTrbWosYn2dDz3fxSSsFnKrU37Dtawfd9d18n+wcIy3vPMvju1G8BRoe01k4RpozCsKTApIQfzJcICgBaaXj8blUkYPj+kzwBA3bwAgV+zZBlCaoRPZ9GbQWSn1WlWkGvR++ZzgsUJTUTRRCMMGgYWD7Qb3gLEIxoeoUAGA1o+z7CJlEq/VEfMqT+knqMxmFUDGAxP9qaZClSHmdrgTDk+19ogsEVGK2YMgSUFjBFqYYDQo/ev6XWFbSa6/R5Y9A7ISXJ0dEhbr9PypCF1ipu3XwDACEqDx8TYnp49AT9LvVRKAPDa9hwWMCyessGEimrHpFnpxDGz2rjyQS7+9THxZVV7LDKJQoUGm1Cb5aXV7Czw/TWTg8ChOTcuHYFS5xw3jk6wmBAcymKY2jmQG7f/ggbrOwcTTKEjMxoATw9PKTrvPoSXt0o0xBS/Pm/+0sAQPf4EMMB3dvGZhM3rl6icds7wGAwnLOHBuD330BAKppTe1t7+NH3fwAAOO4cwWe69Stf+So2NggNgJBThZGYIhLSAmI2ibdMxIUolx1GWWaRlk/+7+lmZ6ViM7//7GdpZz5jZmg76sInFVjCYgbJ+Riu60CaKTYjxPTWJGaQHItTCcliBtWZrp1iBhKajsnzttFkgpgVxUoAk5Su2Wy1cWWZ5s7O9h5uvHAdAFCvVXB5k9CbOK5g+RwpsxKdYNTvcGcmWJa01vYe3sfqAqM9K4vwSwGStQAzH8PRGN0+zbsk1wAjvr700WcqPjneR0no7N65j7XPELo8818LGSJX5aD68JmKssaHUKWUW0Iw/Ao7kz8z81Sr9RokX2dpaRG/+RvfAQDc3zrCWz/+EQ9SiFGPc3iERbWE1zHNOvdMApPSC2mMRq7oxRqNxrAFy5KjCBPetOZp+8cGCQd10vNhNQ1wq1EBpx0hyzIs1QjSS62H7SNWyxQelhfoQYkkQYc/Px4DPV6MVpaq6HToPk/6Oap1Doq29rDUojGpVYFqjWXsJkfBu01fRwgkfW+1tg7BEHaWFtg93J67j1IS1QcAnopmAompxH6STjDhgBMCaLZoYtYqVUfPZGkCCOU+U+YBUE4FBxxeCMW0YJZlyHOapFLJKYUzk1tjAagydhbCwZlFoV3+2Gf3TyFi2jMO/OkCJAwsSxmK3LgcGz+IZ5RccAGJtBL1eoO/P4fia550Ow4ffuGFm5iMqE/DwcCpDIUgCg3gRc+WOTxTpVqapU4S/LyKlN7oBBCsHgoFBN9/XKsjingOSokwndJb5cJX5uAAZOcQRjwO1So0b4gGBtUmbbipTlGwYjISQIVpFJsZoFTrqKmSsho0oFDSvx7GHFgaWFRnxvmzmtHTHKpZFZvyKlhbJbi8Hi/B8PV73UM0WxTYrK1eQBxS3sBLL3wJrSa9T+moj4NDUow8fPgIIVtBfP1L/xlef/Wb9JmJhw/u/AQA8MFHP8TDLcrz6Y4ewos4iM1DR1Fk2TT/iuTt89N2+wcDVPmgVtgcTw4o4AnjGA1W4ZlcY8DQ/DDRuLJJdNVrL97CziOC8vujPrwKPfeKH6DPa2c99LHcoDVmoR4hLeg6/WSCvRN6vw+GRzhfpw1paWEVh5z/c/fhI2yuUP5Pmlo8ekpBVz3y3aH2s5txARqEj2GP1us//YM/QG+H1qz1zSX87V/93wCAOz97F7deegUA8PVvfgurmxSsGWugxfQg5LHPAAU5ZU4kXBgk6fhGXzsTVHxcmTVtAlZM18HnaVlu4Pj/j/2pC35nKCohhPucFPJTv058jHcTM5f/9IAHMwEPTgVLrpnZ/Vg8lwNAvdZEWCp1lXKqzTwzOHeVAtTYehCMEEShgtl7AAAoKh46Y6JhPeUDHzKo8eE9JFeI9o9fvY7JR/SZyEroJZp3kzRzuXKPt7aRcc7a8vKy67v2JXDIh4aFVahLtDaMc4PBwbNVoWeU1lk7a2ftrJ21s3bWfuHbMxGesLEEj/1EhA0gWR0RRIXzHDEWMIz8GBHAyqkvSWEY1rcKmmOr9c1NF4bW63V3HS+MkHA0F3gStpieKEqU0IN1J2otDEqzOV8KMPODKFDQ2fwDsHtUQHLibudkgnMbnMQrLSo+3dv6Yg0jPtF1ujn2TzIeE6BR59OGyXHUo3vrDXLkjMaEkcJRl34eJD7SguC99bUVbJ6nhMVhbwcZR8qBDPFkn34+yXzUA0IosoMOzl9YBgCk4y6Ojh7N3cco8JHa6emnPGKkeYGY/Qw8P0K9UX4GMHyi0sY4bxSjNSTTHnkxTVy3xjrvjdwk0JMpXZTxuEk79cQxxjhUpSgKDNjISkrpfk8+PPOjA4pPVsJOkwVhjTOrEFIhZjRDSOWQJEownkLb5e+NsBgnhNJt7+xgd59Op5VK6JJUJ5MMHl9fphZZxhRuowlTooZiqj4x1oPvT40NS5RrnhaECknG14w8BPGUwi1puCBQqHFyb5BH7nRXr9WR8/wqkgxxUFKXAcIlTjYWBjmrDJtexanuYAQUozrGtxBM5SjlwfBxyeaCnEMBNCt1VPl4mlvt0Lt5moWZUQ/5SNj4ruIt4XPXvwEAePXGLyH26Rk93n6IpXVCKjY2LiLwOencKhjmOgplUQ8vAgAW2muOtosrAdpVQoESleMcI0ieAJYX6eejzhZOBoQO9QZHCKLSy6qPTocUJlk2QRzPn9Bbb8ZYXiNUanv/EDFTruPRAOUiNhkXsIJ+X63FqLc4Cd8HauyFlj4xqLfpWVeEwNExnZZvXL8GD2WKgUSjShTY5699HvUVQooqNVKe0v0PMeG5un+472hQk03Q7dEcu3RuFXJOejlPRkgZ1YGI8B//kpCc2z/7Ca6cI2SgXo+xz+92b28ff8vqtJP9Y/zn/+wfAwCWNmIA9DytCGGd+sYColQqKYdsGEjIKa7j7meGTfpEs59y3p8H69neOfy5/zaLLDka6+d9djZTWdgZBPw0wnO6P7Oih/L/Z/59Bsaxs5+2eC5EudFsoejSXlUMuih4bauGPropsSCiUUO7TshMODxAJAgdr8gAYA+tZrOGWNDa09QFCqY7/YUWJmN6hyLfh8d0dGo0claH1VutmaR3AcUIumeA9jK99x14KJZpjgdeiPr5c8/s1zMDnluvfAEFQ5nWePB5wPI0cTkhRVEgZFO25dV1t+hba2CZ/mgsrGLzAplnLSytIeFgwA8C1Jst/swyDtlFVAoLkU2lwuVjKqxCwYtCrgskrF9L8+nUL7SBfg7orj9KodLI3c9wSH9ciS087le1YjBkquCw00UlpodzeJBh74gephACB0xd1WOgzRRYxQ9x+TxLc5WH7jHlB3QOd7AXcq5LPoEpeIJEddTZU3CQ9iHYkPDwaAeX1mmBbsYG0eJz7CQzm+6s6kp6U6pDSjljwCeR59OgTvGLmyUpMpY3SyGhvHB6TXYWzo1xyj6ttcshMNaekou7nCJjTknRHb30HHlY1tqpiaKd5vOEfuD6FMexW/i0zqE4t0Dr3C0uBQxyll8edU7w1ttvAwCePN1Gg2mCux/dhWEZbyRiCH4/otiH5LmppAeDMrfLTqk/peCVeTJaP1u98fE+KjogAET9eBGPvQUW6y3384TvPwqqDl6XhYXgZ12vxnj86AkAYHG1jqjKsu4ih8f3L6WE5KQZPwhhbSm3B8I6Oy3HsXtuRZ6j4HkRhqE7xBQ6h1edzzwSIKq2VVvkvvhYZnn1N1//h3j1FgU8S40lJEOSwt66+jrWL5NhmbVwu4Q2hVOiqbCJfMIBiRhjwvlIu08OsMeKrdXVNpZZij7oBGjFRI0NT3w02cCw3TrBo+0f030WmVPFGAv0R4O5+7i8WodFaZXg4XMv3QIA9HvHeP/uhwAAL2hA+awU0wMkbN1x++4dBOD3yfjo9+j3Fy+t4fIVVnBeXECZCXPu4iVcuEzrbn2hhXHOuUkSbnyarTa+8Q3KqeweHmKpQZtWPhrj/n2SE/ueRL0yX1BX9I+wfYf+7u237+DhCT2rYHEZlhVpT07GUCHN5aZvMeRg/MEHH+CP/zWtg//Ff/UtNFYu0zWtcIanVqcQzoXfR0lSCCFnFJCnA55T28HMf5RKzVPBwBwa9ePO8ONXnV4T0zVuNsfm532mJOWsMKfWRzGNg2b+Xnxq8CZn05HoAgDIZdod5ayd0nBzNKkUsiEFNjWTk70KgFotgseUk+9FWF+iZxolCmucw1PxJLwRBTNhMUa4RO9Q/MuvoGA3caQDhLcoAF56/WVEy3QIKHSGhBXFjVoDYTg9IA7K/L4iB5YoeK+fW0E9og3z6KiHInp2XuQZpXXWztpZO2tn7aydtV/49szjl1QB4oCzp40HxdFoXK26aNQY44qRLCysumhUCAHD8VSt1cQNLtNwYXMNywwf7x+f4IWXXgIALK2sQQR0qgmEgMlLSkW704hUCmOO/tJs4vx5qsM+CvY68aSC9cdzD8BiK8D2Lp18avVKWXIISS4dEjXZyRFxFnk9rCJhdCLwrPNDOewVSCaEilw714DRFJmmkxDgUhTCaIRsc+/nBR7tUhKh8FIYRquSusXaIp2mzikfD3c4kVFH2OPPX7uqkI3nVU0AutAOUVEz5n7GThPfTpvxSVczK5skODimaH376VNM2L8jjmMsrVC2/tr6BjyOxAubnkIuHFIzg3QopU59b2nqR+jTlMaal/LxlHKojrXWUXCzZSOo/5z8LiUMU6YC1s0jowsM+FTz1ttv44hPp632ItZXqc5bGFQwYWppnBQoWHXlB01na54lqUPIPn4PEz7NPq/Ve7VRR5W9MALPh2WqMJ2kMOX7ISVqIddA8ZWD+CUsdDmshcXqKp2mKtUKDEq0NTyF/Qchn8Qyjbs/u0P3EFdw7kVCVIy0MKX3lec7v5rJZOKOUdLzkJrnQLGsxLk1Un28dOWXcHGD6utcPXcTdU7qz/NjDDNK9F3euOYM4LTRU5rdaPj8Xiop0OUxH4yGGPL83dp6hO2tewCA9bUlLC8QQtJeXHAoZp7k6HHph6S7i8GYzD41ckSs7Ot2jn8+Z/IpbWV5FW+9Q0jO0ckIC1xe5sql8zjqHPL1Q5cm0O11MWBDxXFN47DHKLjno9+n9aA/iHDrRRq369cvunehUmtBMhK4c7CFSU5/66saFhdoDnieh19mhGf36RZ+8n0Skdy8dNWVIyksoIKpR9Wz2sG9d7D/lGr+PXl4G3mDULqoXsfjXaLd9js9LNZpPW1XI+gR06dK4r333gMALP4HD9/+bfrbuCaQDmnsi2SEqEbokB/XXD03SB+S33uBGfQGAk4FiplEX2OBfEohTRHlz0Yks7Rw7y9lCPA1Z0xGiRoX089givacTqL+FEoLU4xqVuEF2CldP9us/VSvodlXz8LOUPef3eJKFeC5UwVQ4Rp6tWoVa+z5hOYCmqs07xYDD/ESzWXhK/jsjRP5EaIKoUBepQLF3nleNYIM6B3S/gIOt4g6VrGPPtNhRZiiwoKMQhfoHNF7//Cki6AUzzRinF+g+yk6Xbz/iBKnv/XCxU/t17MDHsDBYMJa5Cg3TekwNGONy98oComAC/5prVG6iI0yCz8iqPTqzZfw0qvkqmjefx8xq2K8qIqQi/N58F1OhlKeU+tYKdFwD1G7eyjSCUSpDJmhSOZp59YDFOwMmxY5MpacdzoEpQLAZKzRatI9dAcJsoIWo3YkwfMAx0cTl5OzuPE68oI2yPuPO07qDqGdeVlU9bHI+Rb94R4GCS3EB70RwoAdbAOJ0JUmq+DpMXHdCytVVOdXNFO9Kj0t8FrmZxTauIVhltKyFu7nJElx9yPaGN55+23kHGgtLS6g2aYFqXjtDVy6QhthluVODVVyrgC5ZM9qrsoFxloDy+Nc3mPZPqv2jmti6jo6e21jzKl6XH5Zp0lOFVK+H7i8nd44xcOHj3lsgJdfeR0AUK00oThvYDIaoEjo+iedaaDdKgpI/ozvSaRpuSBOVYa0EQl3j+o5FEwLfgzflhy2xKS0AYhC5Px+ZFkOr1S5WTNVhGnr3qfE5ohY3aPzAhkrHSM/cqussdPad0IKJ6PLdY7c0THToE35vmMTJBQyDgiFkIi8+fNbtC7QOaFN+fK3XsSrt2gjlspC8Tu99fSRM8o7fy1ya4CUcHYLeZI4yklJ5VRdRZYiZyXUJBnhhFVLH97+AEcHtBl/+ze+g+svkmrIjzUUBz8nh/vojfo8JgBsWRPNolqbFqn9rLb15AQCZR6Rxd4hHSZyXWBlhXL0Br0BRkM2OfQqmEzoHnb2utjfoSD8/LllrK/TGvPKyy/hjc+/CgDYOL+M/SMKnJ5uHWLr4CFd3/TRbPMBwgucsnZ7e9cpL2/cvIp3fvwWAOD+vQd48WWyvnj3ww/R6893wBodPIQt6PlcuLqCJ0dcZFV6WF6g7zzXXsQh10VKjHL5l0meuDSI7/3NbRwP6OcXb13A5XU6FAXSQuVcI24cg+sOQ6gYAQf7k/HE0V7aaLeOKDlVSMVRjP4xBbODwcAV0d289dpn9jFL0tP1qk7xJJ8Mcqh9km77OC3mKC2IUwHM7F+oz7B5+HkHKWutC4TnaXG1juDSNQBAPY4RsLq43qgjaNHhIJMChxWmQCsRmqyAXD53AR7T7H61gpAtK2CFS5Hx/RARgylC6NKLGCrwUI8pDhDGwLJaNLQBGhuUtzNqNd1zzwG0mIZ99OThjMv/p7czSuusnbWzdtbO2lk7a7/w7ZkIz+72rjv9CvjIGf6W3tTXxVqLEi+P/CrEqITsAcVRdmQ0goATeus1VGqlt0zdwfFJkjgVCpkWslmbmil3Lyw8NmtTno+ClQaQITxHGxlnADdP6/YmzgSr0axib49OcYfHmVOZLS4p5JyoNclTjIZs4uZZpNz35Y0XsHiOEIFxdA55Tve5esV3UaVRGj6P21KthkWuM9QfHGDEvj2j0QkGx5T0Z9MtnN+kU8h7Hx2hy4nTid5ExZ9fipYkiUvoE0IgdyUbpKtJYoxxiIMxFgHXKKpUanjppZcBAHs7u9h/Qp4K6biHvQmd5KTvY4XNzpTnudIeBPFOvS5mE6cdTSmnVdGFnOospJRz0z6UsDil7GYRHkfleZ6jP/Isd8nRga/ceHR7Azx6TGqsxZUNlBUhKrHCyir1b9CLIA09k34vgWJvHM8XSCb0ey/2EQRlxfh8JkHbuqRla+1zJWYXnkS3R6fiMAyQsXpSBT4Me1whUjDOuwRQrACShYZXfpfvQRaM9gzGiBqM9lg9TVoXUx8hKQXiRTqttdsNgBVS2hbOi0sL4xLbVaBQYTpMSoXiOWB0pQQ0114zJnUlM3zf4uA+zbsn7/8UPlPf46s91FfK5G0DafhZGI2EH15RFDClh4wp0O8R4nB8tI+iKJOuLfb2qKTCn/zpH+MLxwSdj7Mcqkrv2VF3B2NOHm7U6wgcxRNgwGZ987S79x85Jesrr76In/70NvdR4TybAXY8ga5H49DdGiJjZdxoMEZuee2ph/jWdyiR++Ll8xiMae69+/4BCoeUeqgz9XZw0sXDB0QbVEKD1cVLAIBz68uoVOgzeW+Am1cp8bR/1IfJShFGFcbOt9749RAVnpvnoxgXN+n03WxGCFlFt7c7wPffput1xhZB6VOVDgF+P3r9CX7wfRIN3L3zAb7xNUp9eOXGmjO0lcUI46T0ZCrw8Ak927uPdxyqc3FjES9cJWQg8iWGfXq2rUYNgy5Tnf0EliWH8yA8Ov95CM8MJTWD8Pw8TOfjv58mLePj4I/7kJ5BssUMleY+8gyE53lo9MPDY7RYNNCDgcd1K7vdDB/sEm20uVJDNKDxvL2/g4ublGT+WzevQ3G9xiwZwvDaU2svYaFZloiR2Nsn6jMZj1GpEx2mtcUCKxHXFhdhOP54562f4Yff/R4AILUT3HyFTEMNfPR4bd496OL1V55tyPvsWlpRDabEjrSFLDcPAAWrQUyRuwz63EtRPimtp4tgtSuxtM4uuyJDn+u1wBRQmq6TdA/w5O4H9L1xC4oL7AlIV7fJCJDtIwAYDWGn91PKX6WdOtLO0wY9j7SoAJJJjibXmwlqFqXudnPVw+EeBSTtShuDYckx+tDVFwEAC+uvItX0t729FKOcIOA8K6BY4p0jc0qeycoyHjwkquhw/wRhyZfWaogrpNzw/AbSAUHMa2sW/og2nlsvfBl+MX8tLd8PTkkky4KLQRS7+lZ5XjiqOAx9WFOqUCxqdcqyX11bx+CYCgr6Ehin1K+drSd49+03AQAvv/IKFG9UuTbIzTS3pry+56kpBTKTt6O1niq2MH+ei1LqFLddXiOMQheYC0yVDLNFUIs8d7kfSZqjvbDM9xXgrbd57FeWsNj6JfouqbC+tsb3a9A72eUxFsgy2hADfwmTbBr0lf0TwnxqHtM87aQ3gDZl8dAcWUknaY2cg8c4rjgjtsDz3KvihT4K5yztUu5QGOl4fS+QLjCT8KA4eEA+LXhbWWzACftmAmQl5KnnVuZQxUEEpeen7SaTFLspUYp37r6JL77xFQDAqDvGvXdp8+vt76JSXeC+WLjdxhZIOD8nSxLkvOmORiN3qBr0ejjmPIAsyxEypF6t1lFr0EKcFDl+zOq8uB7DBnTNcTJAvcmFf6109dEGg54b/3lae6mJPKX1QydDXDlPtFQ1rkCyn0YcehBtXod2NCZMj6pAYf0cbUJvfOk1tFfovTzuHaLBppGd/QGanBc0HiUYcZBcDWI8pXgAJs7QYoPHVqiQM0XY29uBYsf3N167gajCG15/ACHn49C7qcWYx6NVjxGELDlVwFF3yNdLkXPOooV1QZmQQJ8PvVASltfK/aMR/vK7tL48fbiBepXuPc+1y7mbDMfYO2GKKpewfOi6f+cO9rZpI37j5WtY4bpOgdWIPeqrrGhEXNB6nmaKHKVTu5DSrZWnmphRVM3W+jslKz9dynTWmHVqlDH9X/ryT37VxwOe8r+fN09wth3u72KpTfPImCGOOH+0k8Ro1mj/aN+6BJ/v7aWv3IDPcyoZjfG//E//MwDgaHcLrSXKF/tn/81/i69961cAAMPxEP/jf/8/AAB2d3fwj//r/w4AsHn1Omqc79tuL6PK8+f9927j//iX/xwAEDciVFu0TmcI0B/T+7S4sIDldvOZ/TqjtM7aWTtrZ+2snbWz9gvfnonwrK+uuKRlUxjoGbt8MGqhYByFQcmqMxnrrIQ5t9BG3CgNrcZosPdHrRrjeJ9OyFEU4Jdep2TBrd1jZzTl+xHKgilaTqu3K2un5m6YJkOZXANz26CT2qE0XLNGImHUQgiFjBOMR8MYZXEh39coy+iM9S0sL32R762CfFJSbAU8yYoROVPJvdCYcDLio/EIg/4Tvn6GAmWiZwhf0WlzqV1FoCnCjW0CMIV3+93/hKvr81Nao0niKBYppEtU9gycykwpC8+bmgEaUyY2A1nChpCVKqptgg+T0QCKq2LnWYJ77/4UABBmE1y+Qeqaxuo6co/VW7pwKj9rLAo+gduPITMlakCU1nxIXRDE8ENWEAY+AvaMCIIAPsMc0lqHKpF/UIlEWmdPMeiPIBnRzPUYGXsjjdMOOj1CJatRDMMFqzY3VyEM0RnD0R6kR2NjhXR1u6T0ZhQdAmUOtVLT5N55Wr3egpA88YRFXtY3g0VU0s5yWr5DC4GkfF/zDOD5mBc5sgEnDooAihP2lZUQ/M5JI1H06d66/SGqbUIWDSzGnPAujIFK+DqeD+WXfksampHRQZLO/QwBYNDLkDFF+P7tn+Bv/+5P6N6KACpiiq3Wxuol8papNipUFw1A1ulieFyqnDQyRn+zJEEyIARjb3cbJyf0mWq1Bp1TAnBYUWiy4nCQWyxy1ef19SXc/ujvAQDHhwMIRehAXA1djbC4VoFM5+/jQrMGqen9LsZUsAcA+r0eLKuPesMEkpN3r1+6jIePiGZtrTTxre8Q6nXt5jqkLL3HBLqdUj1Zwdo6IWA/+uGbSPndXVxcwOdKJdelWxh32Tsoz/DwMauqnu6h2SbEyQtCjDOuPL25ATknULd+5UWkE0o8F2YCy9mo43GOScGCh3PruMYL+U/eeg9Bm2g0PUlgeC0W0keRl6ahEsMhjfGb72w5EzwJi5DXtbXVuiv3EagQS1yqoNft44dvEwVz7/4WvvI6UR6vvXjF1Wez1mKSz18exOrMUb7Wik+tuy6kOIXgnvbe4bVn1ptspi4Y4LY8+qSY/tLqT37X7Bo6w0Z/AuF5HsSnFgY4t7HK/9XA4D7NF5H6OD+i51t7/za8Mj54uo0Lv0vVz30/wjazF0dbjxzl+3TniVO4FpMEOx/QZzr9LlRKv29oIGGa8vDoCJYT7c9fWMS3vk0V0ONGFecu8FrrtRx0v7+7i/d4H33t+rVP7dczA57R4BBeCcdpQMb0QkrPcxJcnafQHGCkSeImXZ7nuLhJxkKrKy380R//nwCAS1dWsbpIL9XO7hCH+7SRPLj7Ib75nd8EAHzxjZcheaPs94ZIWSnRHw6QZCw5N8YVUIQFojKHJ65A2OhZ3TrVDHL4XFtoMrEo6e9QViB4Ux5OLMb88rVXr+JKheqBVNsvwbAjqkknKNgssYCZQp6GbxCAtJ6jt6Qu0O+x8kH5EHwdKRQgOABTMVJNVGA26WGSEByvtYLvzw/BZll2anOVslRgZY5iyfPcOSFbo5ExZC+kgmHppzEaNZYkCiGdwZlJh1D8ma277yNPqF8Xb72EpfOXaDy9wNF51gN4r0SutYN+gyB0Pz9PfkscV1Dh7PxAGERBOZbC0ZsKgGJ3V2PlDH0X4pCVJO+9exvNCm1qV69fxVe/+Ap/Pke3w3kdykezRmPfaC5Bcz4BcWYs5c+Nc1S21k6pIikdtVgUxXMtQNok0M6w0yelJIhis3K6mIXg5BYAACAASURBVPr8c5plMLwh5jqHsqVSBahW6f0Y7o+Rn0zrcykOfsfjDClTsq2NNsJ66a5tMGC1Tr1ehwhL2bJ1NcsggYzXg0IXM/WK5mk+ya0AdEdH+Bf/5n8FACw2L+B3f50W05c+/yWsslNx/2AHSZ/p5fEEPbZPGCdjmGopk08wYuffR0+fYIcVTJN8KnPNMg3j0cFChU1YU9I3EZaXaA2rNAr0J3RAkUo7mj3wqzDPYe1eq1TQL81HjwaQHo2bHwocHBNtsH94jIwX/eVGGxvrtO7+6q9/Gddv0dqjRe4OhcNBgbsfPQIA3Ly5icMDOkSm6RgrPFY3rlyF5LwWKSzee482m+PjAe6wMnEw6OPGVaJ/+uMU97mgqhdWkSbz9VFIA4/zvEyqkWQUVJJZI82dIPZx4zoF0VtPq9jhPWAwzqHKA4rOEZY/S0WLBgDjFQj494EE1pZpfVxbXcYx03cwBZa5EGu7EiMOWdY/6uFHPyVLgO4wxa9+46s0lmaAvMwHnaMVhZ6RjQvYU5Iq/pc8Q/mpwAIAvXOZAk4n6HBeI2ZrDQJSlPXIzDSgsgKyTBGAcE7RRIDxmEsFYaf2DO62MOPLOEerhaFzuj86GWDEyuRABbj85BEAYPnuCQIO2BEHWPid36bvqtWwsEDPJTJNKKbAwliRoziAUEhUOJ9H+xKL/F3LkzEelQ7rYeTW0eFoiBN2N4/kAL0JzSvfV1Dc98OjQzTiZ9sKnFFaZ+2snbWzdtbO2ln7hW/PDId++ub3EXmlgZfCzde/BgBIkgksQ4+P7t3FZEAQVzrs4tJFMvz5wi99CZfO02nkD//V/47Hj/i0IMgXBCA/g1I33+8e4z/82b8DAPxu6OPlVyhbfmPhnKNdJtkYB0d0eukenyAdU2R3/959TDhCv3L1GqJ4fvRDCIMs4ahZK1efKysG4JxGhGGMIwooEYzWoWoX+H4msExd9Ht9TDh5SinPVQy3WsAaOvFmaQaPo/so8PDF18lnpDPs46c/fZ/Gx9fwg5Ji81DhcgKHnQhFQSfAYX+ELJvPCAwAFhYXkKWlb4jG1CtCOiRFSjlTAkHAU1NjwCHXlBqPRq42Fmnn+HSqLEI+qY76XTz8iE5yhydHuPoiReXLK+eQpgxR+56L7mXgO3pLCnnKi2deBKQaV1BjtEGazEXxSkmXqOypqe9NXhhX8iIMAqdqabcXsMbmWSgmWGEFU6oD9Hp0Mrnz0X1cvXIJADBJE2QFnR5VGMNj1C30q87QT4hpEnVRFEizqV/U8yQtGyRg6xRkRQZdoolWO8+Z0AtcTmOSTQCm9rTW8GWJOBmInOlioXC4TXPq6GDP+SdduXUDtTVKCjSxwTDlWmeZQFQafmmDSanckUC1RHgEncboI3o2X/Mz28b6qvt5rBNn3vjkgzdxYe0SAOD3fuv3kYxozE92d/DBOz8DAPz9D3+IC+do7dlcW0fCfZlkOTTP/a1H2/juD4ii2j45wgrTHstLa5CM8iqtkDKtmWUZNs6RwsdPgWFpPJgPnOKvyBJk2fwo1p0HjyBYJeerEEushBmO+g7JHiYWwwFRLCsLwHd+k9RY1168iGM+5RpYjPkk3DkZoMEV2IfDkaO3rAVqVTppv//eHWj289FaoNen53Xv/h5+8BaZ/UWRj5zVOIEPh+yKRJOh5Bzto5/9Pepcn69Vr6HGVv9SATkLUQJhUKvRGNy8tomPHtPal9sI4PWlXfEQsgmZKSQmExaWGDh6LZIGS2xIFgeeU37G1QiG+arlaoRlrie235M4HFA/3nv8FC+wqnJlw4N6DjWhMVMBhrDlWsj+OeXvU42Ulbf1eoAxK4FhAFkyEwKYRXhOfccU1JkmNtupyS+kdMa+Atb5hGk9RXugp0zD8+CsACCLDE+3ycNpMJqg1+OSMnmOakFjW8kVnEwimrI+UgD2hA2Cc2CD53ir1kbGKu44ruDGF4iiEp6EZrR16+kOnvIz/dVv/5pTeaapwcEBIbUr0TKOdygOiFsh1tlItRo3Yb1n74vPDHiEHkPb0iFSQGf0hbs7ByjrOq4u1nHuZYJBz60tY2WF6tBoo/EXf/5HAIB79247ebtOk6k5Wpq4PBxfWoB51D/6g3+BP/vjfwMAWFxccXWP4kqAapV/DitoNbjOTSOEP7HcaR9LayvP7PRskzJCwUqldJKhucgF6/w6jCF6Y3XzJtq8GDeXXkXCkHeS566g6qg1wAk78yZJ6nIapBAOxktHY/jsSGxh8eqLJPd+8vQp3voJLdyZ1RAME3Y7Q3icN6D8NiTXwgmUhbTTwOAz+yjkjAGfdUZ/xtipJFwIJ9W2JkfIm2UYxch5w5iMh5iMWAkzGWLMrq+enuCEX+5h5wQVNp2ScRW32Tm10dpHyPVzvDjE+kUKhmvNhjP9smaqaPI8b+6AoFlvoMU0jU5HyDmo0Eajwuo36MLlmvm+jwpTV9ZaZ5B47eo1NLnWS+94xylcxlmBOKLPC2g8ZDfPjc01GA76wrCCeoOo2jiqIGXzNc/zXBA3K0VXSrmga67mC5evYlC455YXgGK4XxU+Us6xqVSaLgdJWAOFspCvhQo4n8cUzkgQucTCMte1W1NIgvJvxTQPxxPO+dTz4AJFXyrEvDBlWruCw1LC5VDN01oLVZyUdE9v5PKOgkChwnNEGQXJOUILzTaarCQ56PWweYXerRde/yIMS51Hg5Gjcvb3OsjTcpOoI/BZCmt9Z56qizEytqBA18PSEr33RdaGLehnL6hCl9YEgw7SZH46pJ8a1OJyDchdEGu0heXl2I9qUBxEvfTGK7hwnQK5SZEjs7z2DCeuqKvWOdpM7UxGAzx6QNRbkhYYj+g6H7x7D8st2hgaC3UcdCgofefOQ/THvJaoAAfHtMZfubwOy/k/1TDC2mJjrv5dPb+GCkv2K1EMj910k0xBFGScurLYQotVaPuHeuqKrA1qHHi+dv0KPI+eydOtA+TlYVL48DjobkYSAb+79+4/QMbPOTLT/FEqNq35fnw0eH052t3DT25T8HtD15Hzu3Lr1X/6mX1U0s4UqcIphso5LQcxajx/r1QreML307O+M3oEgClbNROSWMDwxLAWjqLylEBQGsLmhatqIIXBMlucdPpDjArpLiRn5fPP0YrJCPc+pEBUwUeR8dw0AgdteqY1qeAPy/psAjnnp7aWmvj1//L36X7SMdYuXgIAXL3xIlpcA8sXEjdep4BHwsArHc2hsLlJhwwlPHSPaI/5+td+FTdukTUBtME2u3lvXH8B55i2jdIcg+7JM/t1RmmdtbN21s7aWTtrZ+0Xvj0T4alEnqs55EmBDfZ9uHH9kkuOzJIxOkdky/7ez36C3V2Cmra2tpFnZfKRh4KvU2Sp+9KDvR1XakGYAqXTvtbaKYP2tscQTGkFvkDKJRjq9aareRNXqigNQnb293Hp+s25B+Ddj8ZoNBmStCHijKLFW6/8BurL5IcTVuuwqlShCLR0mThm4BKS19YdgmC0mVaMzifO3G8yGiNja/vHD+7hcI89XKTAP/j1X6VxDiL4jOp4Srtqsb4yuPPuX9E10/vIsvkj9kmSTBP97fQUkuf5KRSltB73lULoDFcsqtWycnOBZESox6h3giFH057NXIJhYoAGU4rNhWUU/LQvXrmGBierK99zXj2QwnnBCCFmSk7MDzG3Wy00+dScjQWKiFUcSgKlGqzI3EHM88NT5UdaTZrXDx48RcFqvMbSskva69y/hy6jdwvtBo479PNoNITm98ALm1D+lDYqb78oCpcwXhTFFEWbKTMxTxMWziBRwDh1hBp6GHHi8f5xH32mQqq1CJUK3U+jVYNgpNfzvDKTHiEUooCrw+tjZJaVjr51NW/SLINhJMfzPCQ5Uxu5dQIFKSSGZW29QoNzpRFYCfMcdM+DB4+RZ2XSde5Os+eW1hDx8w1qAXJGLfw8xle+QImnt269CMmIWWN5HWGNvXoKi5zrnVUaDVy5RuqNf/vn/x49VlUKL0JQUtAAoAjFMsrH451DN+ZJQjSfTiJIRkAH/eNPN4n7OU1J4+oBSqFw+z6hMZEXoMI15VpVD6++SKUiao0YP/3pOwCAS9cuoyj9nTQQcH9XlpcwHBE1uby8jgcPnwIAimKMJ48e8/1LXLlGKq0LVy7gn/9v/xoAcHh07BCWRi3CC9cv0f3EIXTI83Y8xqSsd/UZLbESmtc7LeGSWtMMsJLfRSWh+feeH7k0gmrk4/IaoYy9fgceiw964xHGhr2LvAqkKX2n6qgz2mD6ExSCvjfJiqkRZhTg3hatWXsHCepLbIoJixMuLRFkLQTys2touTabDGyBYNYHp/T38gHF65AaHuOCoDn1kR86Dy1gRo0lxGk1Z8G1IW0Bj9/LC8sruLC5yn05Qsr0Y5on2FiiubBYMXh/i+ZCbjy3plPS8vwTNYCCYrRYmTFaku6/JkNkLBBKoJHzGhYagdETNm29+wT/4Jv0XoY6cHSdHfUgPqB7KxTw7UukhkSaQTIrY5SHgPe8/g9+hOMuUbittMASq/OGeYbqmNehD9/H4YQRsO4x1j6j5tszn/Krr76KjPM3FttNLLMp3+Mnd/Hh++8CALaePHT5IZPxGIrVVe122xlsxXEF3Q79vLezjbsfksHgmz/6e5xwsEROuVNOcoENhA4PjtBuE1x348ZV3GVzwldefRXvv0cZ90J6SEqpopQOnp6nPdz3EHS4kKS28FhaLPwN9DnosuMuAuZg/UDB5wVRwCLniZmlxcwmarHA9T3CeghPMi21soCCA7yrm8tUpwhUFFXOKBLKWjvC5m5P9JSP3j5RKR99cBfPsY8gzzI32aMocs8L1jj6x4LqywCAJw2MKcdTocGmbGtrq9hnXnc86CLnvhhhUWLzzaU1LK7TRA4qDWyskcpl4/JlWF7YrDFOESaFRMKUgNbaBWBSyrmVWtW4gohzeAJhkHKg7YfBTEDtT7lw6WE0KvMcLGoc0K2vrqDPDqFGhqi0Sap8+ZLBgweUg9bp99BeYCrEWIy4YKwKE0w4GK9EPjQHAxb2U2u7SSk/UTvsWW3UHzgZspAWCeeZbN/pYO9+h/sikLGhWzcInBWB73vTwNmTLuDZOLeO0Ke+1+oNeFwY7mTUhU7K2mvT3C5YC798hh8rgugCWF2gVM97nkLvOeS+J4cjBH5p/xA79cXO9g4OjkjJI3yDKpvH6cBzruprFQ9DfqbD40NMWAZYqdaQc57V6nIL57j+1NryArJDlk+HFYBzrowxEKwm1MZizGpFWxROUmuFRhiVAZ6YKtTmaFfPr6PLtgDdXoI+BzAGEm0O6kTRx2K1lD1nyPkzh9sHCNk9W0kfQ6YBLlzcwKiU/j7dxUmXNvirlzZQ8GYQqBpuvkA08miSo3NMQfv66oqz5VhejLG5SnO7MxhjyIfU7f1dVDgH7LPam9uPXI5KFCjEgp6nssqtdwMMsZiz+lQMUa2V724NeUFjf//hI3hcl3GSaeS8Vdkic271YdzAUzbEy7UPy0FUXhTwua6W8Ko47B5wn1LosKQuPQyO2D2/U6DenK9/ALt68zoV+RKBKQsRT9WljUDCa1Ew5mU9LEo20vUEBhyAw1rkZTqP57vAWUiBkAu3KmPQYOfyZphgzaO/bS75SHhewG9CMyXXaEh02vRdTw4zl75AXzf/plFo61SbVij4hlVyIsHqLr1PzSyf5kvaHCfv/IT+9nvfh+aDsShypKschPSBhde/BACofulzqC9zzl4jciky0vNQnpiKXLvD4tM//DP4t8lt/YOrazi+QGvzyu0PcO4R5f/kRQ5cOP/Mfp1RWmftrJ21s3bWztpZ+4Vvz0R4BsMU/S6dgra3tvHmm2S1f3Swj3aLq6SO+3jxJYJf79576JKjlpcW0WXKI4xCh1Q8eHAXf/LHfwgAeO+dtzAecv0hz3MVUA2AFicjdjp9B8tVazWY0v/HKmhRnmAjLC3R/TTaCwij6twDcNxXUHzyqHoBjKTr3H+0je9+/28AAEVu0Ob7qVaqLpHx9TdeQ1kDXFmLUYfg78HhNqpcabu5uoSCIdDxKIXHsKuScOoaozMkrLjwgwieKk0UJ+6UI6zF4iLXq/IbyMpT9zzN6qlRli4QMF2R6cyVZAiCENWYvivPEwhvWvW+VCHceOEGTvikPer1YNikMQg8RGz3HgcxBqzSqbSa2DhPFKGQEoWrfi4wZjpBqSmS40npKJ/nqSTue55TA/mhj7REaYocVVaMQE8ROAMJcNJykReu7s76+hoqaWk2mMDw6SKqhmi2aV5oKcGMJpLxGBl744zHY/QGdNpcWmwj4rFM09R9r+cpB7VrayFLVGSOdrC7jzpX5fZ8hX6XaRETolZauucjZ3IZVyRcLmiRIuNyKKk1CEI+CY9baLcJShYaKCqEkAxGPQhvqt4r718K4VA9KSW0K6kwNVCzM+aRRaFRPEeyZCCr8PiUHnt1JIwud4/7UxbBWoAhfusZDCaM4E3GSIas3uqO8N7jHwIAnuzu4HCbjfuqFfT7hIQYAcTsp9RPUlg7VdWJMsm8yF09QBgDy0abBikGnbKkQQLfnx9RtkYiZYQ1yzNEPD/zLKUTKoAr16+h1qBnvbjYwg4rUvb29rHKdYOq1QBbW0Rd9frHaDKacLi95RLyR8MJQkajL146hxqXn/je97/rqJRaPYYXEIK7stxyJnFCeOgO6R3dORohlvMhdaM0R8HPLfAkfC7bE0WhQ5k7RQ8nOaEQNheo1ekzo5MJtns8B7Npgm5qFEyJ/CKFx1ReYgQebhF6M0xztx8EcYg+K9KS3Q56A3qehZAYMJqcFyF8BloO9/qYJM8hAjEGmteYhWoV5xZp/Ky1DrkOpYGMGCFBBF72cU75KPS0DmX53hTGOtQoCAJEjMgqMVVgJaMRTthHKi8K1Fm04wUChsfEWuBzlzkFwfSwtUfz1EgJK+bvY64EFK9h1hgEeSm2EJA9GsPQCOcFVWgBw/XoxkddKBbYTNZDDJv0XC7e+grW/xF57WWjBL5zs5TwmaERo8yZC5sCiJhlSboDpyA8WNtA45uU8NwfC8iDHwMAolGOCptu/rz2zIDnqDPGmCf9cNBHyFh1q9XExjpBSv2TYywu0Jd8oO8h5sXXDwJnziVV4PhmbTTefZc46VG/C+sUHR4kKyuE1RhMypoqAQoeAeHF8CLaeDYvXXdmSLV6EyEvHMrzkKTzBwO+8SBZWreytOaKOz68fw8He8QfjtICT7dpohV5hpDxtzyb4OrVKwCAjYUFCK4Vs5Sk8Ni4zdgF/D/f/REA4P33P8Tv/ObX6bsWGlAcVMAK+Gw8KLSFtbypCIHj0kE1KLDMNZy++OXfRiznk4kCQBSGjsbK8wwBL+JSwFEdnvId9w4rpm6dgHspm60Wvvhl4mZRWDy8SzRPlqewTGnlVmB1mTbR67deQKPV5N9LiFIJo7Wjl6RUUDN5O6fonzn3EdqUyyKe0s3B0WAIzdcOPOkgdVMYeOXLpqyTigeVCItM6/gDi+MJBTCjbIwcJV+u0StrAo2HsExFRpU6Jqz2G6fTQFIqz1EzOs8AJ1eXsM+h2a7XWtPrJAVqbJRXW17CSsx1oyZdVzssjKs47tD9T5LUuY+neYGUA7zeaAzl04HjpHOMqJTt28LlVVhMaUYhhTMzk56AtFPrgvL3Qih4qnT1VojV/Eo0aQL0ejSGh9kIgt/LSljH5sZVvmaECasex8O+U+PYdOxywRKd4s/+8i8BAH/0p/8XLm5S0P3tb3wddS5MmI87yMq8QmugeR2KgtAFbHYm6M6yFKPBkPtrITgnoxYtQYjx3H1MEo3RcGpfMWKJvRDAg8eUb1OrKHz+i6TgvHTtvJu3B/vH2N8j+L7RTF1gMx6PnZmkHyjYIY3DaJQiatJnDo72oHgNvv/gKfaZzsusctSnyIYY1ek6zYUVdDgfbJzLuQ0kI/hQEVtOWA1TBoOB53I6c2NhefOqxxVsnqP9487RAU444NFSuRyVXE9l2lHgQXAOyV73AANNn28uxYhi6uvJ8QQHXFdLa40x80ZGGSi2z7DwkJoyEBLTArxztCJLcZ6DnM/f3MD6Cs2pLM+cetJogYhptUqlMrXekMYJpiwAzX3MUoshzy+tC2eYGoWBW8/C8yEyHsNet4ulZaJn87xAXkwPVT4HXZHv411F1z+ZSBwOBnP3MfWE25tDUMFwADDGw122auh4AqoEHeIYt/hdj4e7GCyyLcS1yzj3+q8BADa+8g2M3iJH/sGf/XuMeE8SWY6wVBw2mpD8jprRBMMhUWPNJ9s49qmPteU6rl6jVInHqw+R2lKR56GfPHvvP6O0ztpZO2tn7aydtbP2C9+eifBcf+EN9LuUVHzv7gfOhyeshi7696IahkzHWCNgOBRPk9whNuMkd0Z8Dx89xgqbu02GPfh+aSqYuyQsKwWu3XyJf/8BWlzO4Nv/8HfQ47o1rZULuOLRdfLCYMLqJ2O0q2M0T/vq564hZwj7sJvhMev7G/UKXnmBTpWFNjMJpsLRdpXIx2hEY3InGWHcIxSooUcw+6S++PDpDv78L/4TAGD/4AQbSxT1v3Rl3Z2cDaZ1V4zWzqcoNRJ/8dcE160vN/G1r1Jl9vULr0GY+WNVX3mAXyZBWleWwFMRZFmJ3hhXckKIKaqjvCnt4imFZa459PVv/hqa7Lfz0b07jrJMxmNcu0UlGVY3L2HEkKSQAiWuUxQFfGdoebof5Tg/jymfLjS4QDesFYgrXH3ZCiRjLt9h/WkVYYgZXyJK5AaoDpuzmLfWUQ9HR0Pcf0BV4h8/3nLIU63dhORkzsFggsGI552Y0mpSSUScDC6kclXIizzHqeI5n9GCVt2VeRHGQLHXRpRF8NiGPss9tLjuVbXRgs8n3k63B53S++qnGTSfcxqNFjxO9vbrCisXSIU0tpGr41xWPi/7UlZstzM0FiBguMaP1nqmlIb5xPN9VhsMhhhw3at6rQVjaD5WKvVpnS/PR9SiPqZphhEbQnYPdzAec12zNMfKAvX9/HobFzcJcZyMhzg+IgokEQaex15ZWeaOfsJTlIQPSvIsLf673SPsbROFtLjYRr3B3lGoY+vpwdx93Ns5cmh0WuSuPI61FhVOnhdKYsgVzO8/uIuVFTrJFzkQBnT6XVhsQHNJC2Nz91wAg1Y5PqPc0cVpluBDLj9x56MdjBP6fX7URYtRoCubl7C+RIhsqoHdbaIo8rTAQnu+NIF+kTvvpSjwHFWokwJixlum4PUrtRaL/J0bmwUaTLE+3T1Cl+eCFQolZ60LKjEEAJWKwM3XyKPo5o01VBn9/7u/u4vjLZoLWlsUjJYEkcU6z/GnTyZIGNH2mxEaq/NT6MYWaNVoXby0uQQvYO8r6zlQWlkBxekO0EMErmYWTiHXZYK3sTlatZLqEq5sj5ICSjLdYyxqDUZRqosuCVkpiwil8naCPidp+3qAzUUa896TCfAcIomK5wOc+hAHDUzYpNFTHjptMhKcNOvO/DCIYmxwKsOCKTBe5KTir/0Grr5GrMbRD7+H/r/l+nj3t125G6+weLJAc+AHB0+ww3N5JQjxxRp91yV4CNjbbLi9h5+9eZ/Gb28XypRMksTjrd1n9uuZAc8kEwhiCiouXb2FnAMeU6Qui9yP61hiqqVWf4xrLPtUSuHxFm0SSVpgcYEmWmuhhRZ37mDvGBnTAH4YYYFly9L3Edfoe3/5V34NBwdELcW1BdRYOfOTd+6gscD5B0LBsLIJNneKoXnaQk0gZU71o3uHmGQ0cRaWWmjVaSGIfA8xw8F+EECoUjGQQGk2YQoCqBpTKSeH+IhNm+7v9uExjdGoV3D7Nv3eDnbdpmJmarEYY+DzQjyYGDx4SKqo0aCGmxcJRl1cuQo/nD9PKU1St/DFUezoH8BCcV9oQ5kGOYbhTG0MbJn7IhUyhk4Xl1fw8quUu7V+bt0pl97+yZtY4BynIK7D2NT1K+dnLYREwcoGo7UrWiqV/FSTvs9qxhgXaAv4EGzsVa03XHBXZIlTFREFU5raBa54qMi1c/fNixz7+xTsP3i4gyHLclfXNp3KKS0MLEOxne4Q77xDz3ZtdRNRPA0Uy0VBSQlb0rPKf66gznrKzTuTFy63ZGIyCI/uZ9AfoMoO1mmeO3dVAzulbKIIgpWUQgiUpXDa55tIFT0faywEB79GyamlgZLgxwZtDHxV9lE4Cb9O9VQl6Uuk8jmk98Lgc2/QIWN5aQ1PntD4KyWws0/vwXB04mqWybgKv073nO3muPsBjf/Ozi4CvuuvfuEFpEyPD3p7OGR1kldvoNKgwEAJA4/HxBgDw0GOtIWjyXSeIOFFv9fVGI9Lys9gOJh/I+l0+1DsWry0sohWWTNwOECd83aWV1dwyLkaK14Dvlfm+RTwWcW2t7eP8ZjuZ31jBQMOAoSwLrDv94Z48Satx2k2xs9+QPWztrb7AKuGcjtGu8U5fWnq1IuZEWhwAFaLBTZWWnP1rz8Zu806jn3EZSFfJVHanvuecnRJb5AjVHSgNWEFkg1kiSIt60lJlx5RaIMhr0HWD3DElgydHz+E4AA56RuEvFlPTIrQo0m7XFGo8bxWElRUF4D0ARnPH5gbKfF4j57P2x94uHyR1rvFxQUEnBIhpWblMVHu5f1b42HWjqJMp/Dq2uXwWDv1CVRKuXfLQiJnes7K6cHCetoF+1leoOD1dJh7uPeYgp/dwxTam59ebmuFQUA3Ea+sYLTL7uNFirjMdzBwAbvONTLek5JXPocb/+S3AADVmzfw5A/+JQBg8sEPgDorTb2p8/ZWovGvOgQ0fKhzTHjO+EWBH/sUwPze6mW8PKBxiN99iLe69N5/vX8EFfFalWWYlAfcn9POKK2zdtbO2lk7a2ftrP3Ct2dCIVb4LhitNZdhGM1YWmygwn4QeZpAcUR/9YUBYF+nAwAAIABJREFULl2mMhPvvfcebr74OfpMkaEWlYmvVeTO/r7qTvdW+rjBNFatvYC9A4JTr1+7CT+iU8df/fX3ELMCK674MHyS19q6aF0J7VQc87TQj1Hwab/VaCBgg5u9gyNs75WJYyGqbArmKarbAgDVSgV3C/IC8qMQVxk6F8M+euyRAS3wuVuXAAAPto4w6VPSYb8/coiDnbEn19AAKyLGicGtG2QWdrB/iN4hq1D2dhAyXTFXs9bVYLFaTz1w1LRQS1FMKS3MJA9LCKTsKaO1xphVMd39fYx6lBTr+RKak+kundt0J4+T446zNveUcv3VReFQIzVTeqFaqzlUxxgzt1GWtRYFJ/8ZXzoln5CSTCkBTKx1qFXZFwBIksSpx7TRTiXWOengKdObnW4XEZeW2NhYdVDyk919HHcZOi8sdvYIkfh//+Zv0WYUEwBqrAba3FjH2jJBtFEYn7qfz2o6yzDMOdlVKhQ+3X8QBKXnJpqNpqvH1FgMsLZMJ+dLNy4j4fn44ft3nE9Rs92EKH1JAg1blpmQgC6LPuvcPTdpAWNLBM5AuFIdHpKMjeFsAS1KA07AM/P7m1y6sg6fT5Xbuw8guLSAlRZ3HlDplUdP7uPWC2/Q/QQx6qw8qkUCKZ/u8twAQ647N5KO/p0gRYM9fIwfudpLujDQDF15voTg+y+ydFrjyBTIUy57kQ7cCbxareJ5CsK/8vI17LJdfrMaYL1CiNzRicTi4nSeHB485b7UcHJCn+/1ulhcpDUmjCu4/5DUZ+NEwAv4/gOF4aAUHwS48yGhOlAGD9kYrjOeIGREQ9kcE1bd9Hp9+B7NybiyiFs3SJCxtf0UrTkpLc9K5ynmSW+K/Onc0c7a5s4/qRrGMFyqR8PicJ+QkywzTggBIWfqVUmAaeT+wKDD5W0MClfLKbQKVzaJLfj/2HuTYMuy6zpsneZ2r/195s8+C4XqCwUDBApEZwYtkRZDZDAk2rIc9sQK2yNPHCHPNLY98MRhhz20wmGHQxG2BxJJUaIggSRAsEwUqkNlVWXf/f6//r3bncaDs++57wNVmS9pyxHOuHtSP1/dd98993T7rLX32lErxoKQua12gKr8W8glqiTDtDTIzepIpLIMx4T4/uj9W/joMxdsvrGx4fcJC+YZa8a4D91YBnXdukXrslkKazBmKamDezSXM8cquGssMkLMFQRSyph1mV+ub4ezHCNCHzWL/Jq7iqVFiozWRZ5mSCuRUVVCVaiOhUdzrQX6b7pA+xe/+SYCqqH26H/6Rxj8sz8EAMjLFuyCSyAILJATLf9/lsf4kOZc0lnDdtfNA8s5bh87hOd/P3qALSqN8uLxI7SFe56L5QyU44OgNGD6yW18osNjwICqBo8CuK5ga4YRwakwCgcnJDLU3sWIilqOihhffctRHtn0BHd+/lMAwHRwgl2qS9VbO4cXXnSNe7x/igAulqLXWUdCA2eRpwgozuD08DHWKDOBMQtJHCm3GqhUU20J6NUzmFIbe1g/bLVwNHWQd14C8wVB4XMD+hPz2dTHf1y7sI21gLJEsI7TY+fMCD33BQjDqIM2DfZEAGPq5MlC+w3d2GURN+s4awC5tnjxMhW+Oz7CmLKJiiz30f2r2Fqv74XwGGOQlVMB44WptDae6lJlgZZPSbSoSA1m4bN9JsNTpJTCGkYBBjQwTaFg5m7zUyJATJRSFIYIaXEXQninS3BRq40upWlyzp9JGbTOdtCexgIzXkU2iCLki9px84VEy9LTPWEQYkIptXt7e2i13Dt44cXLyCn93HLAaOKMpfBjM4qlf5f3Ht7HowNKT40iHyN0+959vPnaKwCA1195mer8rGZWGU9RaasQVO8s1b4Y6CKdo0eO2fHsBJwP6BqDfuIoiSsvX8P+PddXaTnxUL4bcpXDC9/nggnfJ6VSUN4BYLCV4rjS3jGQPPaUX1nWlNAqduHiFg72Ke22sGD0fuaLKT675Tj7W/fu4fqLLkas1+6hKo4Ytto4f83Fc0zzHJOPnUDpWqsLDrceZLmCoZgSpRRMVdNPGyq6SIUhRVWYVXnxNaMKWBIwLFUOSXD83Nil9PynWxQYbGy4WBOmMoBixlipcO+mo+221tb8QfOTj2/h5Zdd7N7GxqbP2JkuckxnlXMikHQou2qtg5NDtwEXpQav4mZMgY2+W2vjtsSC5AViaRAQLbuxsQGQJMZoNEVIWUaL+RwHhycrtU+I0Bf3LAoNXUk7RKIWdFSll0xIrIImKYjQ5mC2qksmfYybtQZgVdFduzSm6pKbIiggqa28lJhQdrEbi9TPVqKkmMKyVChond0fTsHXVh+n0jJYyqrNEeKYaLiTvQWAKmOP1RmnjNWxcMyecTxqMdQ6hsjaWrF5md4CagrMADUVz4S/jzW2ppkY97UembVeAX0Vm6YzFPTddc59nKBdOgw5p46c2zDCgmqHZY8OcecP/wgAMPnxOwg1jcFZgHLPjbtMBHhEiuM/YwUsxabBllDkW4i1dVy45Jzuuw9u48eUNftvaoPLFOYyb7fgRwwzYE+R+mgorcYaa6yxxhpr7Lm3JyM8rK79AwhIoqLGkzECOmJaKJAiOrrMImIO9n3t2g6kntA3F3jj1Svu+mKKSyT/bFmAe7fdyW197QpSuj6al9ggCsxyjWiDStOfa0FQ0GcUMMRh5b1qSEKHBNfotlevlj6cG+9Cd9e2wY8dLD4aHfosMzAgJSonkIGnfsazBbqUDRKKECckBmdViSiuPc3qhN9qJbCVyF6ceMl+a+BhyzAIUNLJZj4ZI6GArG47RofKVWRpCsZXr9AcSImQ+nGxWIDAHmhmoCq5fGVhPBxoMaMMECnlGZ0cSe8qXyxwcugC4nbPnUNAgaSnWQohKkHCGDys9G6kf2+cC4Rh5O+/oAy74XB4ppbWqtXEtTHgqIIarQ/sE4A/JXIeICCJ9jLPPHokg9CXJZnNFzg6roS9aq0gbQuvQ3JweARLwZEikOj23Kk5CGIUdJ/BcApNc8UyjjmNneFwiEcPHFUhOMcbr7y0UvsAQIP5wFrLAEFoa2wFpkR5zNI54m33PAgkRKXXzgwWRIch4wBpc8zTGdZM3z9PFezPOLxQJefcZ/IlUYyiQkKUhilJdNMwCLpeW1PhRIhEBIjVYfTDo0MMST8lTtrgpA+jswU4IXKHh/tIZ26Odttr0EU1hwR6a47G6G+dokvB28PRCBmhc0xGyCmDc54vfFsgQk+PyyDyujeaWxiC2q3Rvj6eNQbal6KweAYgEsPBsS+TsLm5gcnI9ctkssCjR44S3dk5xNvfcrRdqce4R7Wx+v1NhCQSOB4ucPDI0f7QBv2uW1/3Hh37TLft9Q1sblGNonKBl6+593nr/gD3D91aW6Y5GI2H8XiML1FldmZinA6phEB3B6PhcKX2laX2Xa5NiU7bzeEoSWAqwUgLDyZqw7yA6YVz2zjec/0/TFUd2ssKXLm2Ts/SwmjgGIW8yP3+tLG1gU7bjeWP33uERV4F4GtI0o+bLCQEiQHqIvcaWouiwGi2egkUwRhMpaHFWF3tnTEfeMwY83Q+Y+zMGGE+cmAJ6VlGe609q01ma0SrQli10T4InC/V4bLMwkNsVvuyJ8zAJ8ms1MZCYUHo4+1PPwYXVUaYRJd+SwgOweu18OSmo0//9N33YasyO2+/jiQkej8OoEm8E+II795w5alSzrFN9zkX1aWhjqZjsDXHBvFOHzcmrt/f/Pr3IYmybK/tgLdoTfrZR7AUTP5F9kSHRxYnQFnziorqdRiVQVJhQiGADUmppKyGFYOtEOOFg52CGLi64yaqLixee8MJF80WGTY77u92ew3/6sdOlOg333oLX75IWQHMQNDANCwBSHAtlBaBoM4UHIpXYmcKrXj1FMPChh4iFZJhY51EFD+5jZI6RwgGS/TZG6+9gvU+1YFRCiktfOPxBCkVNMvThVcYvbh7DlskxHf34ASgCRp1euBlXQiwcn4UE7h136W0R6FAu+02sG6ni9MTB+mNRzM8gxAxtNZexbUsSwTEgSuj/ESJ4gCi4pY4Q5pXGVWAqkTr8gL3KWts//FjTIZuAKpSe6eRdTrY6rq+jpIEESkwt+K2d4oYY0vUVU1vMcH930qplbO0OJf1xJMBbJVezyUq7Jxxhihy/RZ2jKchjDEIOkQrjAd+4xNBC6enDsYfT6c+m20xTz39MZhNMaGsFq0N5pRKfKWz4Z27dLHAlAS/IhlgMnJ///hH7+Di7sWV2gcAEhJVATXBOUJ6ni6XsKScHF3cRtQjheyYLb0/A0X92RZttCijbrxgsKQwK0LmoXNjTB0zIZmvbcOY9YUMjVK+sLAuNASrDhwcFVAvIKCfoX7PfFFgnRStRRjiYEgUsTSI21TIdXiIoMqcWsx9bTqTFcjGzhEaHh7jgNLPP7j5id/M+pvnMKMYp+PTKUpy8GUYIyQqMDbSZ71pUUDTxpZlOVRVwI7VizJj9dhfxa5cvuAdrVlW+M2SceDipUv0WyU+/cQdBH/1O1/F5rqLXXj0aB+TkRtj57d3sb1JhTaHx1jMXGyYhcA2ZUnCFPjkE1d/iKkC6xtuk/jur34NO/ecc1UsMlzadO/c6AWKrMr4G+DBQ/f+19a3EIarxfBYw6Ar2sXAx8mMZwsU5HRHcQBO98tmBdSpWzdDyzArq6zdAJb6qt3heP0t9w4unutAFW49LcvSi+yFsUYSuXG9mGb47IZzBtf6fbQoc2q6WIDT+JWMIaIYsW4r8sWwVzGlatV2IcQZ6r060H5RBubymuYcodqJXhZdPXPPJTmNioYVzEJW8glGe6dIG11neHFAUS04ZgBVrn5IjlstxOTs8yRARHGI1lp0aO/vtGpRYDANRXG9/Mp5SBIjFjKErrKuwhAgSnnO38HovhOulSbHOu3xFy3zRWOZtdinorhCcOxR/NL8lRew9qtvu897a+AUUzsZjjEsnxzO0lBajTXWWGONNdbYc29PRHiu4yECWSE5HK2E0ICQodNxHlkQCnRip7HTSyJERN9IKfHJHZcV8BefPMBXXnUR3MVsjK+/6TKPRtMZxudJk+d4gOnABVO+9OXfwptfcsiPscoHuFlIcF4J1tW1bZgUUIFDEowqwLC68KAxqg7y0hYtQiQ2tzawv0+nIG2wSWKJm9s7iKp6KVpBE2QYWYa47bzXhw8f4vCYJLH767hHiM1kmkLSyaawAQQFBQZcoOWzkzRmcwcNzlKGO4/cKSvurEFSwKIT/F/95Kxh69pbvAY2OeMe1hUyhCGuqyhKX/ZAGYuSjmlpnkPEDqK+9MJ1TEbO+17MZh4B2b50CQlJg4Mxr0sBC//MURh56FdI6TUzAlvXYZJL2VtPNcb821AwyEkIM0mSOjg6iMCI4gkkRxXIyDjz5Ri2zm/X2Vt5DkW0VFkoSELmyqKWjx/PFxiNHTVwOjhFRvoqG+sbnoKZTqd48MD1/7279zAkVMxojbx4hmxCLWCIquMcAFEzoZDY3nRzKNUalp5NMwte1blRJQKi5CIu/EmPa42A9Fisds8EAEEQeIRHFwaoBNSs9TWtBAtRad1xI8DDKgC4nhNK5yjLZ6jfUypIyqrT1vjyHIGMoKqyESrHaOyQ4wgcKnOoztHje3j/PVfr70fvvIOP77l3fvfRvtcvynmMgoLblTUoaLznOoWla2QYgxGiaTn3Uv7zxcwHEv9iIOmzcFovfumqR23/+Y/ewcGx0yqzyvq1VqkC96jMxOlwH6++4tbO05Mhbtz4cwDAuZ1NfOfbrl7fvfs3PXKxyLRL5wGQzk9hcsrSKSxy7earkhJffdlRYBe2zuPx43sAgNGYYUCU4mQyh1Ku7a1WhJwy1J5meVG4wmxw43RKCKgMuC8/UmoNRpowKrXghBJMZhMYGkeyZJ4O3dzpIunQXLSnCGJ3fasXgKGqm5eiWgTO70a4c9sNzm63BUvzZp5laAfu3bciBlNlC0cBknB1yHy5t421sF+wTi2jPJ/3N2OsDk5e+v/LJXYY5/V3rQWnUAOuc5icSlGowmt9qbJESkhzGArMh5S4YADzDPUXR4KD0V7e7nYRUk1HwMISMmqyHIzWuVAKGNob+Po6JIm/Ci7Bq7p8sqbZs90LuPimSz747JMbWKPwhTVjEBPKPipLPGJuj49aHXzlu07AkIcSUeKuT7WGIFCHv/gCgvjJc/GJDs9vfe8qupQiFsgASasqXigRJ5UjxBAmpGwrYt85YRhikru/D//4pwit26xffPkabn7iOOnJbI7v/qorF//4wQ+xmDjIs7VzHcF5ivOx2md9QJXglD7KoFHSYqe0ATNVsTUO+wwqxJIZx4fCic0pglTPba9X6wbyokRC7X1w/4HfFI2uM3zMkmpuXhRQlOZ4Mpzg5j2X3vxg/wSLKkvr9BQB5RNHYeDjVaIwQEROwmAyw6e33cL9wvXr0JWipOQr0z3uefK6UKkxKFL3nhlj3sGYp6mfyWVZ+jgGa63fmLWxaFPBOq01AnLAthjzE6LV7fsNQGntB7hWxjsBYGyJauI+a8zCQqkqNsKs3MbxbLoUawYUFDMDBhRVZltZ1nw2LNq0scZJDK2qazKfRhtHEQSN9yzNwassiyTxDs/m1raPb1FKISf4OMtycFbFK3G8+YrLshmORhgRvVUUOfgz0JIxOEJSbNbKLglJtmGp6SorwYnaVWyOnBwbo+t3XzLj6VaRSNQJsMwvXkrVKb7LqfPtpIVqV5lMpwgok0+GEqjivwwgeRW/VDwThByFgZes0Mz6HJwkSDChRfxocILxyDk861EXj6mA5rvv/gQ/+DNX7Pejmw9wPHFtLxQHqgy+45GvP1Vq7bMDRRjA+oyt0jtaeVF6Ib40naGaIHVG5bMJZAJAnmfY3iA17CTGOmVsJYHE4NQ5JP1eDDC3pk6nE3xyg2gpJjAmGYStjQ4CkiZ46aVLOKHYw4PjIU6p4PPli+u+EOPBaOgVaXM1BlLXrp5V3pkZDIfIUnJ6RYg+ZXVNJqde2O5pFiTSr2XaKDBb07AFqeQzw1DO6W8bAFW8ps2wed7R4aPHFiKkgsqtALMZyT8Y6evg9TpAWE1LlsAQ5XHpagsXb7tnX4xmiLtEkYQldjbdwRVmhPsnlSiq9dl4qxgzxsfDWK2WUq1+wbFZ/s6SBInx74SBi2rtq6krzhm892YUNFF7eZoCFFaSz0YoSeXfGOXXUxgDRVQzY9alVwMupesZMiYfHB7WBbTYnnfwXUwn1euT9SEyTmJ/uJRR5GU84qAGQaIoQkR7hshLHJCkgLA1RZylGdq0l3S5hKW1WQYC1990Wd9pqXFw4MAUHvf8OhHKGDuXX3hiuxpKq7HGGmusscYae+7tiW7tS29d8ydhxjmCcJP+jryQkpQSLHAeWa5rL5UxjqjvqK5+5xyuXXaw7M7OBv74By44uShLfPVNd5+LF17F9paDWd/9dB8Ph9UpXXvtDKsKMF1VErdg9HlgGRI65kaBACFo+OrrT38BoZS+KnIYyDoYNElw9ZILlMuL0os8FUXpBcuUUh7tUUZ7dKIVBb56bV4o3N0f0+ct9HvOCw6CwOvFBIL7IFchOK5fc3TeFaWR0Ck65gxWV7WaBOwz+KrG2qVqvRwlPadgAkWVwbJEdRljfDYcUNfTCsBQ0Ak8iGJ0+gRLmxpBEEt0lU5T730HgfSV6JVWUEQVOJ0aysayNcLjfne1U9dgcOLrEHHO/YnLMONPXFmRYk7y+2WeezHAfr/nf3M6m6D6cmdJBDHPCt/ncRyjTxXgw6L08G6WZ3UJDsu8DpOxxmtwbG9uYY2C+eaLOU4Hq9dgkkJAEAoho8C/YxkEvpJ0EFSnQwAQ0IQyGWgYVdUoc8/hHnqImSI9LVYHVFptYeg0HkvpUUBmDdJK+Iwbn93DuZdnAnPpIAAcRfw0IbBlY5z7IN7B6cCLXJZzhYzm/Qcf/xwff/YxAGAxK/HgwCE8P/zwZ/j5vqOH9rMCi7IaBPV4sLlCVri5GEQR2hRcnxfK00CFyiFJi2YynfpEhLLI6yrq1tbBptagRfD9KvbeRzdxdEQ196TA2vVr7j5giGmetZMQJWkr3b09x5dfcYhQWWa4ctWtDed2zuHu3TsAgItXzuH42PXjzdsP/Wm8mM8xT11bssJ42iZOBOYEb915vOdLNRwPp4hJX4ibuv7hYjHFene1oOWyLH2ChxAWosoqUnX5DiklMtJN49CeEbTMIKGsrnlUICCNqCgJsKD1N81SBIQqqCLAOtUBW+v3kJKuS7sd4/oVlxCwjxkuftkFcVupERKVttbdwthUa/oc02coDxIHwie6WLYE8CwlY4Cx+hrYegxaW2dXce11pMq8RHWRDEOkC4d+LEZDFFMag4vUi+ou02g84HXWW1GHczDGay0jxnx22EpmDVjFXjDmsr8AL0ILAAu78M/MmYUPEwBfylarwwc45z70QYQBFOn2JDyBobqMw/nM6zINmICmeTkbjvGDP/khAGAzaYHfc8kzSasHG9a0WlUO5YvsiTvKxsZLdbl7YyEJpuIy8lSOZhyE5GMwmXnHYDab4QYVq2MswF+84/j1x48fYJ9Sf7UF/qv/+r8BAARhjOHEwbL/x//2j9AjxyCJQ8iAFtwogiSl5VYSYY0m4Xqng02C+3utBGu97hMbvWzLUfaSSU8zwdY1aaw1ZwasRwbrPnbxDf56i8+7RAbSDwSg3mBcsc4lB8YnSy0NnKVijYwxv0CvZMw5ZAAglhhoxmpV1KIsvaCUFMK3UQjhYzu0Nj7OIEkSD2fCaIiKUhLcq4dyESCoYpOs9ZliQggfEwPG/OdYogc45ysrg+ZlBjWr+ekqpTPLF16oLQhDn1KftCLvRJ8Ojr1DEgTSZ1RV49i1te37PwgCLEhYcZRlfvGdz+eeyutvbKHyustcYTxzi9d8kXpBzbIsoJ/BGVBK+XERSAFJtIEQAsxUonkarKozxRPk1AbLDKryaTxgXoAsT1NMiRYOo9DHMgkhwKMqzV+h1HW/KYqrkIn06sRqUdSyEEE9roVMAL56G0eTGYqsok+BIiWnghUQCcXhqBL/5E//GACwuf4B9kmR/d7925hWdfkC4Q9Dcdj283h7ZwOKHKfBaAi2qJ5N4OUXHO04ng0wJYGzrhUoKWvJhhKZqpz0ANaL6CmIYHU65OR0ipgK+V6/sIu9AxfTFbb6GA8dRXH/zj3019xhkQuFv/7Xfx0A8IN/+QNYUiWeTSYojfvu5vYODo7cuLVWotelvjAFCqLolQFKGifjoxEmLdcWrU8xJ8qEWYaddZKLCGN/SGq1OkjiFSUitPUFePtrHQgatEbVh4+y0LDkpZe69HOUc46CHDTIAr0tysBqBWA0jlqxhBRV/Cir/AWMJlNf7LdQOWLqk+3tGEy4d9NfDzE7IkmDVoidXeeoalZgPl9drBa8Xjc5r1Wgz65frJb5YHVKuFUL5JSyPZ2PURBlms0WPjAsDENklJ2kyxyMsvqYgaeuuEAtfaFrFXtmjE9Ld2toJSNR1rTXChYmga/pyIIAKl8qGuyD2RgYr/Y8Be/YMO5DIkqtfNyfhoElqesyy1FUiubcYModmJJsbWJE4q8jZaCqguJZigf37wEA9oIQnGIPYxkhoEKunbiL9tr6E9vVUFqNNdZYY4011thzb0+upZXG0BWsn+cwgsouqNxDkvPFApM5CatNpv6EPB6PcfDYQfZhVOBnH/wZAECZEg/2COExDBd2iB5oBfjmtxwHtZkI9EhLp9WO0F8jKfZWF4gqHR6BLgUgdtsJSAIASRig310d4WG/EB2/nL3DPdaK+vP6zzPf/6UAtc9BYKyLaqt+rMZalqBQxhh88XTGPO3FYJfoQoZnqbStl8TRtLFeu0IXddVtIYQPdjNL7VKqrtlirUVCAWhcCFKoA6TkPntOLaEWUsr65AELWVEyvyAiVDWFC4ZwKUtr1bIER4PjuuI6Yz7oWwqB0didguMkRlAFO7ZanpKdz+eIKEgujmOfSVIsZVAVufLikbPZzPfDeDz0QdlBUGeV6fEQlkQry1KhoDpvs3S+lI1Ri4utYqpQPstNl3UtsHKWQZeVnkzkAZXTwQwz0qRQdubFABXXSHSlaTOFDWl+pxoZaUoFoQRkpVtfw/EcxsHnAGxp6n5kzJf20EuB39oAJVbPDJmMU8QkDglrfdabFkCbqs9bxvBo36E6t+88hKTSHkzWJUqE5FjfdGtAt93D3p67vtAzXLnqaOpFOcTmJtXhanVx9QWn7bJ/qLH/kaPJ8qzEOmnUrK9tYEBZpMZoRKFbe05PJrBs9TZqDfRJQNTYEguq/zUcZ0ir+jU89sJ5X37lAvb23PP89C8/xIVzLpnj+uV17PQdbXN0OMbenkMRd3bOYWuDyky0Q2wMHGp0OJxhNHF/DyY5ooIQyDJFj9bLOIpR0Lia5ZkP5F2kCwwGq5WWMIb5zDxV1qi34ByqrPR56npPQSDqqt/WoqQg1e5GiJiC63O9QETlCRgCjzZ1EglD2j65Lb0o32JRIJIVE8AgYqqVuN7ChAQX8yJHb4uSAHiA6WJ1jRqd12uAkMLT+ZxzlFmVVGNQUh/GceKreE9O95ERwlOUKRhRzdzUa26Zc1SwCLfGU9kG1guggvE6mcfAl7UB6v1jGSFnjNWJBSvY9esvoOLAsqLwe0AQBJ4FkVz4/UOGQa0FpBQyanuW576skdbGswVWa0jKGiuKAvceuyBkvXMehkJkBvnUI7KA8YkUulROHwxAYRXUlNDoPId6Sh3NJzs8JkdRieMZA07cdgCFqphP2GmhT97GTieA1s6BMWYX89dd+vmCc3BT1aEp8e4HjoO3CHCFCm5GcQRB2V5rooUNih2SAUOb0pytcC+/emEt2qiCSECHleJqAYjVUiiBXxSCApZdl6pWEGdLqYGMot/hJijRfQdtAAAgAElEQVTDLzs8jqNa2qyXHJXlfNba4Vl6BtRf5bzOxrIwy77Ss2idIV1ktdCfYMiz6h0qT01aALyaZMxlsQCOYg7IgeBcQpKjwKU4s3n7zFxdb4SMcf9iBOM+BuKXslqqCW0MvPgt47ArFn8ZnRxCEPwqg8A7M6EMUD1AmtWU1mxUix0aYxEEbrwIIc9kSlROlDY5UuJtB+MhJDmhWVH4ToyTCJJorMF0jEq1eD5f+EwuACgoBiZdpN65WsWYlbWAquV1pjgHBG36QgKSKKft7R7EhDavrPTxNoYB9ybuIFJyjoTuWXLt4ekwrt+9YNwrDGtjwIiuFExAolInDlFU8ro88YsyNwYtVOmsTzfBQ+RZtVm6ApIAoEWJhDkHIy8KFFQMMp1n6G+4NUNKjrJSQgbD9k5V7JchmVMtH6kgE7dOXLi47p1YzgBQ+uudu7fRIao8jEq06VAlpMbmOXf92kYXswlJYgQJtqgg7CpmobG55WKoAmNw5YJ7h5N5hh5lRW2ev+TT1Ts9jtOBO2hevHjZr3nfevt19EhN+p337uAxqZ4rWLQjFw+xu3nB01v9rQ08eOzueXI4wfbmefc+yxn6XeeATaczjGbOgcjyIbZJqLBYpOhUcvpPMVWaqk4x0jR3FItreE11cuHZFSH5GYcnpnTjQAgv6qpgfYbqdJoiovmtAo6ADhZJEqJQtBZbIIlozYKCqvatENg579raiTVKXjnLDJ3u6gvq5PAeqomvtfHUfhgEPqsPukBJopitdhsZ0eB5mvtDbBQwMGqLKZVf06MoRJq59abUxtPjkMxnLYFZv+mbUnvfh0np43uMtj7zUix9voqdP79bF2EWwodEcLZU41BbBPSe1zY2/PputMa8am9Ry3sopbzArlYKJR0Ex+MxJlPnED44OvTrZa4KP05a7To7ljGB/tqaf9YqJEJb6zNxv8gaSquxxhprrLHGGnvujT1LRerGGmusscYaa6yx/z9ag/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvTUOT2ONNdZYY4019txb4/A01lhjjTXWWGPPvckn/c+//Xv/mU2SxF0oBax1n3PGYHQJAEiiCFIIAEC+SCGY86Es58i1cn/DQsoAACCEgDHGfW4tLBgAgDEGKYW/BvS5tRbWGrqG+99SWoFz97dkIRj5btpoMOa++9/+9/8FW+Ed2BWu+fwv2rNfZf7f2v8/gwC2egpm/M+JQrh/AgBj3vXMJVByd00EIDDVbwFa0OUAePXeqhf1BPuHfzS3Rmt3PWdg1VcY6j7lDIw93f+t3u0Tr/m8f9gvuqD+wEID1jWSK4u3XnWNf/1q64k/+u23L1vAXbu+3kO+mAEA4lCCcfdVYzRE4O6dJDGEcQ80LwvMVAYAaMUtQLv7SDB0Wm0AQBB2MZlO3X1sCUb9o1Xhx3JalphmCwDAZm8NFza2XDs4p/Hsxuwid781n89RlAUA4Pf/6YOnvtTv/7W/Zqf0DEVeIsvcfaI4xuuvvQ4AmI1HuH/vnns2rRGGIQAgDAIkrZZ7J1GA2XgIAJjOZgijmNpSghs3p89vrWNnax0AsFjM8eDgxLVRCfTWd9w9wwjT8RgAMJmM0O/3AABZlvn2WmtRFK6NN278/Klt/MZ3f8NW8+bM3GLWjQ0A3AKM+s4ai0C4MRvxHC9dPA8AEGXpvzpcpDCBa+P5i5fx+PAYAPDg8SFk5N6JBoMydH9YCObuH3AGuj0iZhDSP5gIkGtL78TCMre2/dm/+v2ntvEf/OdftaOxG5+XLm2iVDQO5wW63Q4AoCwVsjwFACyyDPnC9XW/m2Bzs+ue2VpY435O8ACMCbpPCaXcuhtGEotqLiQCnU5M75b59RIWMIrWYwMwWmuDkCGr1m8LGHrn/+C/fPeJbfxf/tf/wZoid+9Jaz9XAAtJ6wuz9VrJOIM17ne4ADi9e10sIIV7r4xJ/7yLdIT+2gYAYHPrEmbzku5eInCXw2ggoO8KACp381KpAkK4La8oSuTGPWer3QFozP7ab/+nT+3DH3w4sZKeU3CGgNrIrfH7H2P1eipQj2f3+S//xBetq9ZaaHr3SlkY7e5prIWitae0gNLu+2Wpoeld5apEXrj3UxYGJY2Lv/c7V57axt/6vX/H/nt/9z8GAGzu7MJU84O7Nc0/t9+/ud9LGFu+pv4pzln9b8sBmtNgBRh3z/bxBx/j8NERAKDX76HTcXMiShIIWqvCKETg/Qle3QWahdD0Tv793/jG57axQXgaa6yxxhprrLHn3p6I8Fzc6SEgt1kGEoIQFc4ZrHEnN6OU95psFEKQZ8elBI/c7Rnj0Fr7+3I6dVtjvf/HBYck75uL2g8TQp5FeKS7Js9zf00oOTR5oLDSX/P/pTHDUMMYHJXDLuwSBsOYO34AQCig6aICQEnfZQACj3gAhtVfre5fAUOAOz089dkYXzpp+Zud+fOL0B3OmT/dWWv9SWQVpOfsQwD1+/n87zJWnzwZNyv/Rn+9A0twmQkMWmHknrcowJgbC0nSxmTmEJJ5ukAvdsglFxwhnQYl4/UBpFSwdPJ9+1e/BU7X/PSn7+B04E4gQggo5k5QgbWIabyDGZwOTt3zaIMKJQ3DEEXm5o1gEtUpcRUzGlAlnb6DAGlWn1pzOsGmaXrm9BXH7kTk0Fn3W604Rj6nOco5LCEDuSoQ0bwMpIAlBHeRzj1iIyA8YgbAo1t5nmMymfjPqjXDWnvmeZ5mDs39HITHGlhWfV7/ruQCly5dAAAwPcIpISct0cfurvvctse4t/cQALANjvX1TQDAwfHAn1qttQ7eAAAGxJEbP/1uG4rQCp5PsNV2/ciEwCh1J9K0NDB89bmQZRk4q1BH69uSJPFSmy2CwKFzkeUIufvdfq8NRrO/LDJw7sZbFIXQhBSNJ3NMJm6cX758ASGhWJyXy2dthEFCfwaYzlwbVakR0xDWOoeg1YUJDsjVxupGYP36lGWZHwtKaxhCGzjqNWWRF7DW9UMYSQhap/J0jjCI6HVwKJqL09kIMbVJcu4RPgUOSQgu4xZFOgcAzOZzhLxaAxL/jm/dvYOHB3sAgO//2q8hCFdZSelxGPeovWXM/728ZjEGcI/wsDMIT9Xn1b+X/+t/Y2n8V+NFcAZmK2TOepTMaFOjKxBIEoeKpKMBLK25FhZGr77evPHaGzg93gcAbGxu1UiOZWAeyVnaR2Dqfd1aTx0srwFsCV/htkbzlC7A4MbgT//8T/DPfv+fAwA63Q5aievrKGlB0jra6XTQ6zlEudfrodV37W13+7j6wpef2K4negYvv3DVL3bLC5eFgYZbuIs8A0wFQ5aw9FJlwMFlBY8GAOpFsILapeC+M5VSZwYCXxo42tM6GowArF7M/W5tIVEq97fWGowtuwRPt1+kpj7PvhBypLYzy/zzqDxHNneLr801CmoAVwVamhw1zVBUi3ivhfamW4h5kAD0Di0HClTOHiBpZhmGmiZ7OqP1xEvODFq2NGCr9tmzk/JZHB3OODQ5BNxKAK7fS67AqV3CGII3AXvGfVui3p5iYWwxXhDdUxaIJG3oFpDCTZJFtoClRa0sShSKKFkWoS1D/5uCoNKNjU3owo01VRh87etvAQDWen3cf3AXAJCVKQ6O3KI5SyeYFa7P0+nMtylJEj93siz33mogJLRVK7UPAHW4ex8ykAgCSfecYzBwNE2R1XSGMcb/Luf1QUEIBlZRCJwDwrVdREC/QxSYFBBLVGT1W2G7C0b3McZ4Si4MQ+zu7gIAJpMJSqKUwiBAtnQwWa2d9djzj7A85hjzTrpSyrd3vduDDQnWTwUm1HcFJDQtc7NF6vs3z3MYmpciCPxiLTlHv+cW0CQKMZg52g7pAiZ27zOA9ocSyTn0iuMUAGQA9PvbAADOBIxOqVkGMiInJwphaPysyQ4Kcq4eP7yLtTXqo5j7/i3yEpLGcKvT9rSpYRYBOW+hrK+3ViIK3UZy7sqXMHaPgGJaYDpw4znPDhDQARRWQ6maJnySFbM5ZvTOFtkcoPdaFCWqM5fkHLxytPMS1VmryDh4RReXBUpZUW312DeFwGTonJnF5tQ5YwC4tdA5rbNG4+bPPwIAPLx7F3Hs2trrbmF93W2Uj+4/wIc3bgEA3nzz6zh/MVmpfQBgDGCrCWLPrlOaHGfn7NQHV+/KGvO5+81ZZ38pAsA6+hIACm29o8gYA7FY0Ib5/a/MJ/jJX/wLAMCrb74Na8ghNNofnlexb7/9Lb9vwWowmkMcNS1kjYX1+5PFInMDKUkSlKWbf8PhAOfOOaoZS2tSKBgKOrQxVoB8VQimMBw4R2s84h7sILLZ/cX5EijCQGdadNpt/If/0X9Cn//257arobQaa6yxxhprrLHn3p6I8FijUVae/RIUV5SFh5iN1SjpFNdKIsSJO1FIAYgK4ZHCBxiXZYmicJ6dZgwBncaVKmEqiM5YVGCPNdZDz8Zq7/pyJgEKFtQi9h6l0dpTb/9v2jIkeeZz+u9kOsNnn9wAAPz8g/dx79ZnAIBsMEJOB/lWUeAFOsH0ZxaPT9zJPIsEXv3KGwCAt3/3b2Hn619zv5VIHyipjYUg2NrIpRPGivZFyMzy55/Xxmf5XvV39f84LFQVeKgLhIVrrxABrKTgS8ZhKjqPacASDQrr4din2SyfQVHArVYaipCHXqsFIemEKYw/7TAmPVLBWwFyQiQEhD/1nwxGiEJHCQ2GR2Bwnfjd73wH3/n2d9010wmmROU8vH0HP/7zHwEAjvIjaO3GeBAytCj4OU81krAOalwEKzUPALCxcx5FFYxr1dI8CNDruyDOkRpC6xrd3Np0gdNScFQBz1maIs3dfaIoRqvv+qHVSvDbf+M3AACn+w9xcuhOWcn6eXx6yyFaTIbgsgr6VP60GYYRvva1XwEA3PjkBk5PHJ135cpl7O8frN5IwKOk7Jc+qj6vg0GZBOYz955DFiMkJGpSTnD/jkMqilJ59GY4nvj+zdLUoxZxkngUKUgS/9vTyQgnx4cAgE4okROtwrn2DJjVAoavNk4BYHt7A8Y4NMYYizAmOjVsIYx6dE+DkJ75S9dfQZ6658xmC8znAwDAPEsRxe6a+WyGTtv1YxSHuLB7zt0HGqOR64utjRaq5V4KAVW68TwZT3HlJbfeCLRw9+aHAIC9h3Nw5uaRtRbcrHY2Ho9nSAlhSvMchQ9lYGgRjSy4gKWzdhgIVMGrDBYhjS/NhKdAoqQFQbSUCo0/phujPBVllAUnVGx4eIDHt28DAAYnR6Dhjl5ngoMWocwqxVrXoerDkzHOX9hdqX0AkBeFpzE1YwCtMZLZGnlYovDPjA7GYJeSdioG5cw6y87SP/VXLWxJCJip34OUgWMYAOT5HHpW0eklNNw7t6ammVaxD9//EPce3QMAvPXVb+PVV77i7mmUvw+DC2IGgOl0gk8//RQAcOXyFR9sPJtMsLNF61AU+bm1mJ2ipMD8TqcNSQxQWZZQRG8FIvRzVzAGRvsfZ8bToNZYoHBjuduO0bJ16Mzn2RMdnm6/6yFjrbXPkOJcQqmKuLSYTl3WR7sTIYrdNZ12giisV/SqoVobH2/DYQFUmVwGIOfHGOEfzQ3iKltA+T4zSsIYd/9CwL8Aps3nDpZ/XZambnL/5bs/wzs/+XMAwAfvv4v7d9yEmw9OMV+4hSOZzPDNtttEYyS4NR4BAMYo8I9/9EMAwJs/+Qn+3t//+wCAy6+8BM3d5Oh2ehDtNQCA7EV4creetWW4lDH2hX8vX/+02IsnU1vVRM/BlFvE9fznmN//fQCAydpoX3vb3Wf7dWTWOQTSFM9ADtS2FrVhg5Z/dlQOjGXgjGgCyT2HrSODjDKzDkenEBXdU+beWS6KHOd23MaRqxwFUZFxq+XjH1pr23h454G7ZnQTxdjdPw53UAr3DNPZHvLMQfw6U7A9ygzS+kxc29Pst37nd3Hjg3cBAO/95TuYjJ2j1e2t43d+5/cAAD/60z/BeOg+h7V4+xvfBAAc7D/GPYKbtdFYFJRhYhTU0G2gVy6+gW++/W0AwI0bNxC03FjbWFvD6dS15fDoGDEt7nEcY23DOVrZYoHDI+fMzmYLSIo/CcIE7BkOH8sZnMvmYPMlR4jVdFKbnEmBAI8eu74Yzk9RUF8nyTp2NhykvrGxhcXC0SFbW1sYUtuLPK9X7iTBZOL6azYeoSTHIOyvQ8QderUFtHZzulQGIMd4FdPGgFNGiuCs/tneFrZ23gQApLMpIuYcuXYSeqL3jddfw2jknm3v9AijkXNiL1y86jc8qAkWqbsmiAXWyaG1tkS9CjMU1K7R6Sku0NjYunQFyr7mnqGYYHrsKB8pBCjZ7qlmGUdEFFIUx8jpdxiTiMLK0dMQRGkJJqCUm1sy8MlS0GnqqRzLAUV7gEbh+1xK7kMoGLcAzelsfIrKyzk+HiJc6wMAru6s45jiuaQE2h03fh/vH+HF154c+/FL7fQxX3wpNLE+pBlj/MfqTBwLsBynVo13F79YGTtz2Fumt6p1V3ABTXF2ghn/DkPL8G99/9cBACeFREVEBkKgzFfsRAB/8Af/GMcjl5157YXXwGje29L6OL7FYoHp1O1h8/kIDx7cAQAcH+9jd9fNuSxPsbbm5k0raSEM3HtoRcC1y9cAAKenY2hVxeBatBJas4PaPWGG+TCXrNQeHAEsupH7/NWL6xgdP/mA1VBajTXWWGONNdbYc29PRHg454go6E1K6eE3KQMwVgUhG/T7zuO2KB3tBBfYRcwCBBce6gvDOouKAz5IzTLjqY1CASkF6oWhhBSUgWN1nU1kJLSpgnhrz9oaS8jRvx5bpm/KssRHP/85AOC9jz7CrXsO+j8ZDJFSdsfY5kiZeyeRZGjHznu9URS4tUWn/c46OMHlkwe3wP7H/w4A8Gvf+R7W190pZDaY4Ou/+TcAAJd+5TWsFKxMxhmHRg2jntFR+DykZoXAZGN0raG0dB+HGtE1kAiMg1e3xR2sX3Lv4fadmzj6zL2rdf0b6Gw5ZCE3bVhe3XP1PtyIWp5e0VrDiArtER69ycsCJZ2IGKynNqyxWMxcsHGCCGHbjeU4iZCVrg8Ho5E/tcow8XB8Nsvxs3cc6vLB+x/i4NBlb82DBMGa07EJxSZU7tAPzoynN7Xl/pS9il27eg0n++50mmUZtNdIsT5jYWNzC5JQVVWWCGistbsdrzOjlEaV22cMR7qokg80xiP3HibjOdLUtf3R7BEePXrkfjcvfbZav72OtXXXxtPjY+zvOwpsPp97ONuYZ0Ox4jj2J96z36tPdNZyhERjxILBlA4JsazlMzo2tvsQdDo0VuDihasAgPXNczCELKxtHeA20c6j02PsEJq3ubOLEaFnTLRw5Ypb/9Z661gjtCSfDvDgk0/cO1EMcbL6udEY4el6GQiAqCKOCL2uQ9VacRvSUNZbmXudmiAW6G1QRkq/hf09R7eFYYCIMhNVbgE618+zOcKkRtuqfFqLGGMa83EscXLoTsXd9V1sbzuaZ6/TxdgNZzAhMJvNV2of59zrvTAGBLzSZDIAzT+jNYqq34yFIbREK+X3D5dVShSGEH5MCWY9mi95TQmmWYoH9xyqPjw6wmji7n8ymuOlK9cBAIdHpxgRArqzveGD8W/e/AyvvP6lldrn2oI6m9PWWIxdypyy1nrqSi8hOai/emYvEUKcWXer9YnB57CgVNrT8oIJSBrLhcoxHLr5N9g/RJfoze7uVRi4cXG6SDGaT1duY14sEEQUbmILVBHnlltP4ea6wO27bg5Nx0eYTh0idHyU4eEjR28lrRCW0NCXv/QiLr10CQCwvbmBTstRXSfHE08vB4Lj5SuXAQD9RPo9xkJUwD1uPjrEbLGgl2nQarnx8OJOgLsP7z6xXU90eOI4riE0UUPTjDEIiiGRMvTOD1idmma0ga7iaoz2aZNGa5QVpM54ncYeCHBZZdEUmE/dS2ptJnXWleY+Ct5lbDkT4E61CoBiDHgKj/ck84N3ec+3dilmyfio/M9u3cLPfvoeAODOZ7dweOAWjuF0hAVBjPNcITcOau1wBkmT7HRRgnXcAtfe2kBPUuqp0SgplXMyHOHtVx3M/dnBKYJWlabJapHDVYQAV4jDOfM5fjGep3JK4bOPXAI58agQXviM18lqYAjQMu79RKP3kBItNDMWsXIL6Mb8Q4g1t1ENk5dRlJtPbc8vmpDSQ5UBAkwzt+DOswIhOXrzyQwl9YkVBjyoHPAQSeze6253ExnNqnE6w3Dm4NpLl76M/gZl1sgQE0p/vv/gMaZEaWawyGn8ZgyYU6pvL5QIWZXZlKMbOuekzAsUz5ClFYYhCkrrXXZaszQDo7+/873v4rObbiO++emn+IM//EP3ZWuQE20huPBzudVqYz53/cAYw5zonpOTE3zyiVuwTo8PvcihZdzLQcznczwmJ4cDuHr1qv+8MueYrT4XpZR+3Ekpl5gC5TM0DAR6RGl0Q46UYlSyfI7tbddHX3nrDXS67pq9/UeYz93zD0+OsLHrFtM3v/Yr6PTd/Dt6/BAvfulFAMDa9i6OT12/G5VjreXm4mgw9EKLQXsdsuVokkRbyGD1YCzGpKelgkCi33Pj/dzGDja6lFFYhEgnrr+GwxMokgBJehFo2KKDCCXNm7JUKPOJ/43tLee8RfMZHu27WKb22jrOX3Rt73XXsLFded4KwxPn2Sze/79w9ZpzDtZ7PRwHVfzHAu32allMeZ5jThmTSpV1NiYDEorhiaIInV5SvRBkWZXV10JMqceFmntqMQhCL0KoBWBoA52MT7EVuPm0GJ7iwU1HwR3tneDOA7cWx+1NBNKNhY8/+BjrXRLgbPd8llMriXB6crJS+wB30OWy2hdlLdsh6sOeNaYWV1wKHcCS2C6shaH3Y8zyQZRVic/u4Ecfc85RTadSa3JlAKtLrG+7sbzWXUPA3f95eH8fgg4903KBfYoZXcVe+tKLeHTgDjoP79xC8Y3vuP8hAp9p2ko6PvX+9q3PPM3e7/edHAtc1t71i248fv2tLyOO3bjLsxnayRa13cAL4wJYJxHTi1t9pHkVStDFaOycnLsHJ6iC6KJAYqPn2nh+s4OP7j3ZqWsorcYaa6yxxhpr7Lm3JyI8SZJ44ShjjA9gLsvSB21xHi5FmteR9WHIwUN/RvP31FqjLCvRQuuDAnWpfODg0fEAi4Xz+rc2+rCG7g8BWSFItoAmxMAagemMgkQzC1M+mw7P5+rPgHmo1QlHURYQNEakA/Hezz7C3j13glqMxkjphFyYHIvCnSplFiEvXXDkK1EfGXnovYu76PacV852NrAVO682Lgq8ePUKACAdjPFH//M/BABcvXgOG7vueiM4PC6+QkzoLwpdLQfKLdNbVVAY/yXkp9acUEvUVejl6TUsZSXpYoIycydkPT1CwhxSoIoTSKqT8eaLlzAfukB3kcwwoMwTG5cABWmzZxCN2JsO/SlbCIER6TvMFxnWCN6N+zHCkvRkwgCau46Y53No5caj5ECHgsqn5RwxBaP+yje/iR7J2c+zDCf07ON0AdaiAOatTQSpO+GUkwm4pdIAhUbGKiSnBNdujGholHw1bRPXljmOjg59GysaazKbY0jPc/X6VbS7rr3KWoxJaHH33A7OnXclIfIshSYkUpsU2rjnTLMxfvrTnwAA3v/gfRSEXIWhxPqaG3eD0RghBSRLKb3ejlEK5865U9ze3p4fU0qpZ0J4zJJGydkgTu4hVyECRJE7sW9vrWOdEIk0T5FQX4xGI4xGA7qnwogom5PTEXKKvj13fhevvvoqAGBncwMd+m6URDCbDr05PT7Gu++/DwDYf/gYJaFk165cwMsvvwQAuPNoD5PF6sGgSpVYX3f3txaISXtn/+Ftn4l04fobOD5y72FtYxdHxy4Y28xmiCpkIYixQ4jWdDZeogIZqvjP9bUEitZOLUK88vJXAQCTyRSKTtqbvTbyuRu3p7M5Tg7dqb6XJF4rRxuG/lpvpfZdunQZaeYQ0CxLEYo6Y7OS/S+KAqwq/RCFuHrRjc0LF6750gxHh/dx7/49956MReE1sRTyCkXUOe7dcuvL8HiE0yM3D/b3jmEJVd3aOo/9xw7BGo1mKChwV0QcQeCe7dKV63iV+nMVW0bGjYXXYZKM+z0D1ksQwZhakNCC+YBtV9pnCUm31T7EUMM6ymcpA4FnRCRjKB45hHV9Zw2S6NZZqZAq1/aNF85j+qnTIxq+9xkOzepI5Nfe/Boi0qyaHB/haM/tc7uXr9XPbK3X4EuCCAdEe25vbuIiCX/++ve/j+995xsAgDw7wnDwGADQ6l05U4KmQqmNMR5FnkwmnvZPc4UFxQNoVYLRWAo5sNV2iNZGO0GRHj2xXU90ePI897VwljtZa+UzrUqVQ1Spx2B1uixniIhrD6PAZ78EAYeUpISr6w430Mgo7kVrhZhohizLfTyH0Qzcp2PnWKRuY1VK4uDULdAnY4Mi+6vH8FQqksLCw3LMGgiq8ZSOJviTP3BKkB/99H083nOL6cHoCANS112Mx9CTSv1WY40eeTfuYkIw8UUZYeO8W7Daly5gM3Ic9WL/CJxiQcqTU1yktnfHMww+c5N7+1s7FbP0V2vjM6olV1dxyz1vzNQCKNwGrKafYDFxAzmbnUAXbqNdTE8xta5fru208MKXXeqnkBxl7u4zYx0MUqIH+Ba4jzNYvQ+HRemF4Mo0Q1lNDG0wI+enCBj6HbdoD0dzFIWbMJs7a/rxf9oAACAASURBVDDkhFijPRzcSfp4+7vfBwB879vfw4j6NozbmJCo5GA0wHDqNotxWmBKGyKsgbFuLKe6gKDsw1QVOB4T1B4K6GJ1h2c6mWJGcRf9/pqr/wPgoxs3MJ27Ns7mizrFPpB47XUndXBydIAJfffi7jlwcio/+/RTHw9x995nuHXbySp02l1cuuQoqnReQNI8vvbCts9KjOMYGxTDMxmPvZNjjPGb7y+KiT7Nvng81sJznIcQ3DkJg9MReOnm0/pGFxEpXW9v7lC6s6t3pifO+Xlw61M8oFg5Gbdw7bqjsazReHDXxX8wIdAnNebZdIibt9znRZrh3KZr7/HxsY+JC8MQZpat3EagRJviURiYly8o0gyDY+dgb118CZl2bZmWFucvXHO/ZeeYDB0tkeYlSnLUwyT2jmU5yyG4W4+jVgdpRpRurjAdujEwn02xSMnRHZ5ie909z+ULO0hp7phc+XWdswBCrNaPabqo5RMYYFDVfgIYPVer0wJNP6ztXMYLL7p+YFxgeErUEuPo0Xw9HQwo9ozyl2hd3n90go/fd2n0p8MxujQnLl28AsMcdTWYLfyBwIIh7jhnOStzpBQH0klefUaqw9ZxitagOnVqpcBoA+G8jiQ94/AY6xdUR1Hp+nrv/PDaW7KiSlIGDENOcVBcaGjaWOS5No7ec45NOp+ifYmoSxPheOG+vAg5RLn6mnrjk5sYUxZgr5egRaKbqqyVwrN04ZXIkyhCQll4v/JvfA2/+Rv/NgDgjVdeRTp3a95gcoxOqxbI9OEvy5QWZ55CH+nMy0UYlkJXyt+wIF8VMYBtOqRudRMEwZN7sqG0GmusscYaa6yx596eiPDosvAnbSmkr83TabdQUubAPE9RVFH51kJ6BJthQR5lXnAkCenPREvaHIJ5EaOYSy8YNz4d+CDRdieGpmDTslBQunJ3tZcM11biSuJQgvPnma8i/Fexwn/VIvCZXxrFxKEAe5/ehRk7z3en20bRde2aDzUWBOnZ8QJy7k5Q3Bq8TvodfSvwKSEFl4XAS5E78YRFjvEdByXj8RF6dJJ8/eolvEB1Y/JHe8hJAA5vf/uZZMKxFEgMPBvCwxiDJURABSnEqTstR+MPEZUuQ+n44B4M1YgKdA5G2U0RMgwn7u+jhwz39xxy1V1v4WDk3lXWeh3nX3YnkhZaYKgCc1evpZXPc6Rz9ztCCFRDxGiOBSGR41mGlILli7REQTL0imv0+248jkoDTtkRFy5cx9/8zd8FAKz313Hn5B4A4P7DPfzwz37s7lMqZHSfR3sHyAn9YJx7IUSlS5gqgE9pGDqVa8tXLU8EACiVwpzQKh61ceGqq5Behhvo7lx07eUxoogovCDBq19+GQBwV4Z4tOfg73MXvgQZumsePjiEoMDzMJBYUGbWxsYuvvKNXwMA9HcuokOV1vcf3ceP//SH1EZW62RYg+NjKm9R5AgpU0xrjQ6dqFex5VPuGbNLJ2qt8PixQxMDXUDsOqqxyAa4SMGRW/1LSGcOeZuPThASHc11jqM9Rw/Nc4WsyloxGg/vuoBXYxR2L7jxeO7iZbz6itNnufnpbR+gubm1hX2iBY0MAb46VRCKAKI6+zMNQ8d3ITkWC9LlOrmLt15xejjTaerL6YiS48quG6t7B0dIab1hJvQIWzuJYUm8U3CBLgVdF+UCh/v3AQDdfh8paUOdTk4xHbtrktMW2pSlOJ8swCmoXgQS1qxWIsRY6zN7BY98pW/OhadjhJTotl2/vfrm24ipgNdkOoCVVXA690HlhVKYEZI6OD6FJIhkPs0RSYcChaFFmJAmUxD5sIaT40NkNG9a7RB/9z/4OwCA9fUEf/7DPwYA9HotfPapC/b/3gptVEp5poGB+fKICsYnzHDBPT1ntfJrmdHGi1YGS8HuWuul8ALra2YZg7oGlskR0VguT49x61+6ubjX6+IH/+SfAgDuPHqIl19y9Nzu9QuQHbc/lSnD5vaVFVrnbKE0uiRoOpnuI6PxMt1b+MSbjfV1tKjvup0u/s6/697tb//238TueUdpcQBBtc/FMcoypbbX2lXL2ZytVsvT9Vvd2FNaIo4wJ+QnDAQYPUMvDrFJqJHQBfhTyko90eFptZJabFAIn07eare8QpQeDTw8aorSp9MB8OlruoBPO8uyvE5Ll4GHxzQMjinrYzIcImm5RZkzBh5UdJgAYxG9JFOrK1uOkiBPKf6fZWhVeoocgCbF0NODPWQDB4t2hMZ3X3eL4PzSDmbX3WYzu34F40O36A8Oj/HwoVtY9wb7eIU2WjWeYTB211xWJY5+7Bbcbn8Du10Xsf7Gm1/F1asuda9cjHH07gcAgOHte9h5o0qd1MAXFPv8PGNLUf+AXdmRcF/mlZAo/m/23izIruy6Elvn3PHN7+U8IhMzqoBCzawii5NEkZIo0t2hlrsjHIpuWxEOh7/84fBH+88KffjHLf842t1uO6KDak0tUVRIrYkqssgiRbKqUKgJQGFOIAHk/PKNd77n+GPve15CXQQeusM/dO4PBir58uUdzrDPXnutpfauYHD3TwEASf86NHjQDkPsc39JGESYnKJktdVoolVhNlFqYTCgixjuOrjdJyiiOXECE7NU0kYSG6juSa6xBA2XvYSCYQDFG1CSa3jsFZUrGxubDEu5jvHUyZBir89QlNCwNP3uz/3C17C8RAuE55Sxukpj8NqN76BWo+S60WrhjTe+DwDo9XdH/UewoIveFTWa3DVZR8nhzUtkhrY6TqRZbuAqxyvj+ZdepeucmYXmXqAsDIxZJCANeyRNM1SZ8nz0xCnTK/fMc8/jyAIJhNVqFbS5hK0tF8fOUEJVnZgD+HCzsfHAHFaSJEaHoYIojk1vTJrncAv/Jtc12Py48Un9dEJok7DnWqGweLItgTAkmKbmO6gzbaXpA5J7AK/euor796j/IMtSJBEfnnY2cI/L33kaIYvo3j3HghoSrCLiBo4sUH9JyfVx9xYdOA5uTkEcwfYfuYw+FL5dgiMLU+UUqtgUpTCU7M17l8EIAiZmz2DICXyShqjyn0qjAKpgsJQqxrDTdmxkGcMPcQSp6b1MND30hrRphUNtVJSlnSHhJDzrDdHv0zy2pQWP+7WepA9LetKMd0fnUBmzA0WGlCHcTj/F06tnAADlcg2KWawEgdF7r0zNIOJ3m+9t4cEaidr1uwOUPDbjdVy0uC0gs0d+j50oQq9PyWBv2EcSUsJz5pmTOP0UQbWua+OXvkYyH7ubm1hbuzf2PWZZBln02+SjnlEaTgXL0DLzTGhtBCb1geQ9yzLTx6L0gR6nNIXDrQ9COYYxbCNEvE9jM9/eQ/8tYsC+/vFH6Cf0bo9ZAkuXCd5yN9fxQ3633eVzeGXx7Nj3+Ornv4ABMyDffWcTm1u0n/X6MbzC8809hcGA1oAvf+lL+MIXvgiATD8LKFsJAZeLHW6pioxp9Z5fRmGzd1CA8WAfHyXIox6nAvaS0MY01tIaIqXxEw97UPmj2wQOIa3DOIzDOIzDOIzD+JmPRx5NXNc1MJZSymSjnusZfnzV9VFm0atgGCDiBso8TwEuL0nLNo1mcZIgKaTEpY0ynwaSLMatG9cBAN1OFzMLS3wVwmi8ACM9IMsCbMYEci3gqJHOT8EmGycO2i4AgM96Qfl+F9uXLgMAetsbkDk3RW9sYHiHSupptws9YLfjzgBVLr/JOESVy9DnGg3YNyg7vpPHeJZPjC9NLeLEElWHFmYW4bOXyLA9wLVL1BR9//4teNzgLaIU7m0qSas0hngCOfu/H5biE6bUyCS9C3o7LFoHy5C/ynkP4TpBVw8++mM0KiwlHgbosHy76znwqgTDVZsuJlkTwhYCKWNvsRRo79Gp6+O1u1AtOmk9fexlYykiEo2Hu7HHw3watQrAp6xKpYoh/54PIAqZgZVrTHATZKnqGh8iIVzjYdQdDNBiufnnnn0RkvWlVAYM+T1nmcYyS6K/c+EC9vYIprMdjawQydJGpwtQEpJZE5AuUm7ijrV6WOvpMRGnObjfFpVKCaUyC9BV6wgSKvdrIZHyKSjLFUJmpHQHAUrMVnM81/gPNSca2NolaObih5uYmKGm8tWTT5vKQ65yyAPzo5hb9oGfKUX6RPR5YZpT/XINg2B8fZOfHpqF6IhUUFR48ihGyKf31pEFLEzRPVpZiGCfKhV7929ja4OuIUhgXOAFMgz2qKKcJyEmGlQZW56dxNIyQWPTyzOIJf28XG4gZchyb2djpEuWPhkTrVT2UAiZSMsy/kCOIyF41oVhiFt3aK5PLD4D12VtGmUh0XS/S0fPYn+fSRKDPqKAK12uh3qDxka/N0AU0/d7jsRkvcJ/18b+Ho2ZPI1hscaObQuoAsayrBETFNrAeY+LiYkWinO0p3OkPG+Cftc4wKc5jDN8nsYYBgw/Dvqm6um5ZfNcwzDF7g5B6d29jqlcVqoV+Kztk+e5ETxMFTBg+CPX2jTHPv30KdTZ2mUwDAyj0REanc5Ix+hxkScZuIiG1NKweZ/LJT1bgERPC2HcHBkcVTBgLWgey1mamWds2Q64GAfX9pFzVUSHAbYvEZlgcbqE3hVqor97+zau3qOKo6VjvLZKe8lycxm7mvaMd8MtXLy8BgA4NjWqno0Tji9QqnKlPMtw5x5b6MQxzpwghOOjD97DpUsEBf76P/mvUKuzP2Kej9om5OjvBlFoWEHiQJvFwQpPHEYYsO/fVjwwFeW810NWwBTUAU/PUwJNJgFASCTZowUyH5nwdLtdIyTmuiP6eRAExnxOaw2HcVdXSIBfVBhr5Lr4jG02j1ynSNk8FFmC7Q5BPN39vcIDDM2pWTiMx0ZJCqFGZoeFEm+e5+YhEYV85GFiWU9WuDJMEq2h79PEuvbHf47uFWJFiXiIOKTSXbi7BbdPSYIKIwSc+FkQyPhF+b6FhTnaOEU/MGyH+eeewmvHCL7x+gmiB7QQX7vyE7R5QuexhuT71XaOZ/4xdbsnSYKrd2mwn0tTeJoTnjE3zYfN6ZjFJICc34ulbbNJ+zqCDKnEu3n1r1DW9G9b9EYyBamDhBfTyelF3GBPKUdLVHgRFypDwlTIO1s9vHeTvufjOw/wc7/yZQDA3MJxY1BrwT5wQwrjJjzDOEDVokk+M92Cw5iudizcuU3JqSU92Jxch+kQjhyJ+NlcBa3lNl56+lkAwMmjJ42icrfbx1/95d8AAH709gUy/QFw5+4deKz0mouYGBUAshimz0ALx8BkNnwU8m2Z0Aiz8enMuYKhG0vbRc7ZUpSk5t+ZAlJ+9nGmMOCenFq9iRIne2mSotumcafTCHYh/CktpGmhaC7gsWqxlBZcXsRt2zabkO/aWFikRbbUG6BSY9qysODy77p+yfQv/eeFMGJtCpZhwtjIUOb3OFn10CjxAj3s4jorIWdKYHaRZR622ki478W2cmQpJQlpHMJmQ8+aLeDy4cZDhsYUzeMwH8DnXibPKxn4wbYT4/M0Ttj2yMdKiJERapblB0r5NmJmv8RZglqT+xn9CUg+LCItAQ6tkalah2PWQgmX52itVkbC8hhaJwDPs2q5jgbfy3a7B8sujjcHhGNVPlJfFdokGY+LLEkMszdKQ/S3KHFLgh7sCh2KmnPHUOZNKgi6BpbMs9QYUU9UWhiyMa9WFjzuO+t01g2kYlk2mUiD+jtdj+Z3FEdwOAG3pIU6U7ZXV4+g12Nmbw4ofh46V1hcWhjr/gAgCAP43EMiHWEO9lpoFB0VOtcEawHQIkPMcJ5rOwZ6s6U0jLY8U3TgA5C09xBco8P/j//m9/HMeRKf/WAIvPe31Hc0UBkG3NLxC2eew8lTxwAA345DfPMt8nQc3nsAzT1O0rPhlQqpwsfH73zj/8JTp2ivypXCg006QCzNz+DznyE4/Xd//1vY3ycozdDx8bD6tFQwyUkUhbBEkRPkAOgdZVluJC5sy0a1UgiLCgiGfzVgDnOO1UeMoldKoMEsLce2YdmPPnwcQlqHcRiHcRiHcRiH8TMfj6zwBEFgKgOO45hmY8uyjF6NUNp4fbSmJlBlyfVBFCBi5k4UADu7lK3v7m4hiKiaEQ9D1BhOOLK8iKeepZK6U6ljwKfNvf0NXHnvHQCA63k4cYIad6vVmimJKSFG5cw0NSyBccN00CuFq99/EwBw+TvfhbdNJ2ErHiJNWTBuOESXtSr20wQxS5UP4wRPtwjKOb68gPvMiNDDDI1Zuq92O0L7PjWaBZ0urA5VuqxMkGQ3gAw2SvxacpHhWz8iMbhNGeOZ6Rm+YjkS5hvfjNpEIooyqg9HF67FAeyUsnjsfghvSB4p2HsLfW7ulLaNbW7e3tvdwfIinQB6cY4ey8O3/Aqu3SaYx7VdDLlsd31zB1fvUPPo7MpZPPcSOfpK7QC60O0QT9ZQzZEhw+oZOgXNTCxge4c8p3I1QJrTuKuUZk2FMkkBm+G7XOVwuWLjVRx8/jOfBQA0Gw1zMlE6x16bns3O1j2sMdssjBI8c56YUFGqTYVEQkCwno8SGhl7qflOGRWLqxOOB+WNX2JOstyccJQWpnKijXg/YDsuXv4UudCfOLpsTqFRkpryPTSQs/6ISgIsLxMjaWXpKLa7Q75O3zSGaimN59DBNSDLUkTc1F+t1oyIpnQcSH6eGhLhuDbbjwiNh0XZiiHfqjg4NkmVgsXpKUw2aO1RaWqYLZXmFOYmqTHbqTTQY3dnaUts7dB77OYxBFcwBvttTDVZ2LBRgc/N7es7fURcLVFKQXKp3fPcJ3Kh1khNhSrLFDTDPLZtGX0hKW0DXybRAD6fWoUlAU3329nrQln0b7dUM3pKyDKkBUvSt818KpVqCAKCl7r9IRx+v55fHnk+qRQDZq5VK2WUuJLS2e9D5ePNyzgMzbWk4QBbm1Rh1UkEzdpkZ5ZPQbGzeZoHBma0JcFzANDRFjSv6VmSGVIK2avQL/g+0O+zttAwMHMiV9pU1T3fxwRbcExPTyJg6E/ariG9WACyJ7B5ibMUGVdnRZTA5/XRsyW0xd+jrRGqkacozG/SJDF7lSMkHJ5PAxVhkV3d42Eff/y7/wcAYNaawL//kzcAAFtJgElGL7721V/CZfaN+r3rV7HKO/mPb9xDyO/Zrk9Ax/R/+OUykmB8vagbN65gyGZqC3PT2LxP8+b8mdM4c5rWPK/kwXFHQqRGCypNDRJAEB/9e6I1g4AJAVmemaphlmWmYuqXfLj8nc1mBQGTDMqVCnr8rg/I/QFaweH9o+RIVCoFaeOT4zEsrdEvW5ZlSnFSWiP2Vp7D55ubmZ6GzyqiQZphgyGbm9cv4+a1NX4YIbrMBGi2WvjKV0jcbWVlyVDXB3GC+9sEddVK85hqfgEAQWyFUuYwGGJyYoKvR2DrPuHZcRhhgntjnjS63S7ev019Ow/CHbg9wvitqI8Wbxil6QncYQru8OgiNrkf4oUzz8LZp8n6zsWLmCyEz0oNNBnGuv7tNzFZJlGzZrmJIb8oS1iIebKm0oHFTLcst7DG93sh2MHROVq4heUA/6kJjxBwuZSY7G9h+x4lk7bcwZnVAvt/H5vbBFHVGk0ILovu9/aRaxoTlj+DRNCiefvmA+yx+nQqXezv0X0tLMzh6l0yV92NMhx7mpKJX/wHv4GFIwQdJbEubNAIrjzAJvtEivInRGOihS9/nSjkUk/gt3/7fwUA5PmOEa4K0ggl3lC80oH+J5VhwH0gC4sLOPU0sUcsSxr6aL1Zx+c+R2XcjY27+Pj2Gn1Gejh5jCigd+/fQ8KSAxAwCayGMph9bjlIjQedDf8J6MxQGTKGl3WewmFmkO1ICG4ocGo1vPwqGbFayPH2j4k+/+Die3iJ+45810aa0PfcuXkdP/4Rlb8r1TpmFgj68R0PWQHV1qqIWOVWaweOQ+8/HOxjr02L4LRdQsRqw0JbZrOBlHCe8PDxSUGAFtObdQ6bpQumm3VMNOn7fd83Bx0FjTp7aa0/uG0Ug6fKHk4eIbq34/tYZ3r7XnsPi5PEkmw/uIOQN10HKXzQJrE43cAlVfS3CDQqvM7tJThQzX9skFjgyDPJYSaUIx0IXo4FJCTPUaFSRANe27J9BAx7TE/OordPz9yr1CELmDqMEMVsAtsPDS3cckoos4SGFhb6AUERtu0YwmeeKQMtSDnySyyVShh3oREaxucoiQMD3+1u7cAp0Txbv3UVNRZ3rLemTEKt8hRD9s/avH0bq8cIptE6NXtPuVwxfWRRFI9EBXOFYcCtBlrAYcmSKIxQXpjg+/CgWEBRSHckEqgkavb4B60oiUeQllbG30pJjZx9vtJIISyWaNuGww/Zd9xRj6ntImZIyI2G6NwlGCt/9x18qkF9ZNfDAeqshv5Zq4SZU3Tgf/G/+VU8c5f2oe3//d/iL96mPsuSVYZfePc5PmqT3E/peAgGwdj3aFsAOAmslDycP0fr9Rde+yy5IgBYWVnFMBiJvK6trQGgQkkhrul5HqZYudySnklmiLE3akMxSXeeI+A+4H2dIOFEN4piAwsqpQ50cmiMOlgO+JT9lDiEtA7jMA7jMA7jMA7jZz4eWeGRUj4kDV+cuLVWAJ+a3LJjXGHvbd6D2qXTyEa7h/V1qpB8/MElRD06NcXhAPPsnfIPf/XrePW1lwAAWZqgx9l6q25hokHVjHa3h8GAKgnVVgsBS+Tv7ewZyGGqUYPFzYiJsDBbGs/ZF6B+syK7HO630ebvd1ePwOZy9vs/eBPLXHrUClj+L74GACifOYoqOw0fmTmKK//q/wYALNgenDNU9vvuu+/hs9zEWXJqSLiRtBcEsAvWgs5NYSPVuWl47QgJ59QqAODZibOIWVzPkeKJUlUlhGnAtXUOOya47c6lb8GKqZKztLiCzm16RxubAXYGhc7EDkREnw8jhR5rteTawvoWPSvH81Gp0ynKLTWwtEoQXpIpzC5TdWuhvoizL/0DAMDcynOIs0KLBMZ9Xh00lHgCUb5KrY7jR+l5l+xFzE5S1eX171zCU6eo2idgIS9oEEKgzGMkioEBewkdP3YMDYZF4jiGzUcHlWusHKHqx8LCAvb3qbJx+vRZTE/TSTVMIgQxe3gNQ9PvKQEjQJalKVJucC1JB8M4HPsepVbQSVER1LC4jC6ENmdvAYxK/ypFl090udKoclXr/voatrZZ72rQR4ebOJMsg8eMzA8v/AgZnxLPvPRZTDWp2dQVwuiPKC3wzPMvAgCef/YFrK8RjBiHMWxuNCSx0vHnIoCfWtWTDJM4WqHmcUXL0kboLU1T3L/HjfbrdxGw8OD8zCSqB5o1e9ts2wKJFosiVqctzMzQmqTDHrKUntvd2zcww9DYyVMvof08WXW8c+EiMmOhY0M+QZnVtlzgQCVHOHQS1sKDzxpKSZyhXqdxWC65SHic3F1fwwOGiD7z6s/B4Qqhdiqwagx7eRUkAcPjuYJX4yXeL0OqQhdGobe5Rv+WORyuRkvpoFymdVcKaiIGAM9zRlW7x0QwDCBlwRSMR2KZrmcYv0F3H5vr9PchBBxm+e63u7j0EVXY165+TExfAM1GDVlenO5hhP56vaGpMjq2QJM93zIlEDD80ekPUWW41XY9KMHfg9zck++VoTF+JTLNEgPDe07JsLGCIIbrFm0fNuy0aOkYIuYydlwqQXClUN+4hmmeH/OVCj783g8BAH/1xt+imtK7ev7UCfyjLxMKYocRdpnReul3v4WPOnQNV7Y2UGLBUdcvI+Exu9iYgSwx5ORYT2TzUi1V4HHjdzgIUGam4NmTp41/1vzcHHoD2pOkFKZ6MxgMDHtSawWtWvytzgFLjty4Z+R5jozLZHk+at5Ps8wwtNMsM1vCwTUi1wI9flb9lBjGj4pHJjykgFiUmjJTVhQA7MIQ0XKR8gUMU+B9ZkdcfP8SunuUwNR9HzmXyI+sLOHX/smv0QNbmcP1GwR5vP7t1/Hu2xcAAC+/+LLB93504SL6jEnqXJl9/uSJk3jphRfo7w4HKDOTpDo7h1iOLwQGDciCGtjpwGPq9PVb65AVmhBbjmdUlxNLYfUZMh0UtQqOsznfre/+HfI1Sh5WXn0V/89l8nj5Ufs+rn37LwEAX8kqCFkkKYGAVfxdkcPiTowcOTRjnl2/AsVsr9X5BZQ6NLktqZDzex0n79HKgcgpEbXTm3AVlUKffeFpuGWCmXY6CrttHqTLp9CI6PODvVsIB9Rz1VpswrtPzLU03kd9njaJZr2BoE/XnOoYbb7OMPMwtcSsp/NfRXmKyrGxErC4nKylxuitKoxNOzsQvUEf27yJv/zMWfzGf/0bAMj3ZXuTFHTzPIZks9lSuWrot1rnsHkRaTabhuGSpgops+XiJDW9K81mC5OTlOScPHUUQUgJYK+/P6L0OhqK5Z6Vlkb5WwOI+eftbhciH38BypQe+cpAYlioOjs+HB47CsIsImkcY/MBbY5ZHBpm1p2bVxBy6d9xPUwylGPbtumJa7d38Pabfw0AWL91BfOztAkmUYJswAyvJMHJ40cBAC8+/ywuf0gCmXE0hCygAiHMYWjcMH15B1lBQpgxYkkFnRWMF9dsZkEYYcCGof1uB5MNOqxoy8XmBveUuSUM+NCw1+mhxSq0rXoF16+QWNtg0MUv/9IvAADmFhewzwrradDFzASrWPs27rGIZZapJ0p4PNeHKOQOFNCaJvkNrzKJMjNTtZawDMSikXG/y872PeicxtuVyz/C0gLBr5VyDSErplbqTUiHfQhhI7fpHm3HQq8wVM0SM7Yd1/rEQ22SZgbWLPmlsb374nCkdptmEXI20S1XfdT4MOGXfATcS7Vx7y4qDA/ev7eFd9++CAC4dvkyYk70nn/2vBGuFZaFASvdx1mKIR8CykLDZ1XpPAGG3B8ZZMBulxN/7RioM4y2UPbpfSbDEE+y7mRJDDiF8aU0atmezACXktZsqGCXR6KSBQU5aXfxKK6aFAAAIABJREFUFMOYaMbQPUqKfvDtP8G9NVqr/H6C52dpXj7/1FnsVYmlF0/6WFwhmO9v/+SP8G//ghhbnldCpUZr0jDI0WSFar/VhF+y+TM2oic4YIncQsllh4DahJH9sIWE5xTq3Ql2dmkvkVKafkDXddHep7F2ZHkZNrcS6LyCAZt8p2kK1xlBWkX/jxDCtNJM1H1E/H59vzTq4aEHT78LG1shK6CnNnLx6L3/ENI6jMM4jMM4jMM4jJ/5eIzwoAPBR8YkTkwntVY5Ms68wjwDWN/k7r1NXLhATbAP7m0g7nK5vOzh1Ek63a+szOKdi28DAHa/s4X1e6Qt0+n04XmUfb/73vu4zSJ7nTCC5IzSsWxUGB7SQhhtgA1Lwyu0QiCNrsRr+PJjH4AWGgl37j945138PF9nZbCP779FJcbJaAA/oufw1LGXscTllWtvf4CERQjzq/fgcan1Vr+LfIlOxUtTddz/gMq0O9pGs9DssCRy1hJQyMmHBdSgGfNnekJhfpVO0TpVqM1Spg8pngTxQUPtwZXUHJeXKlA+wTyBqKCTsoZL1cZk0+evtyBRZOIBFLM+qraDoENVrOvvfwu3LpN/S8VrwWFvHFVyUZ56DgBw9NjLWGCoyXamERdZPNQBfQX9EHwl/qN/PD62drfx53/+h/Q3Z5fw/PP0Dn/rt/4XvH/hJwCA9y5+F3fWqfoYJzH29pitY1mmdJ6r3Ix3S0pkTJUJwhCaqzFf+MIXMGQn6+2dLcP6yfLYnMptLZAVOhFKQPLNZBkgeWyGwwD2E2jUOF4JNuuM+KWKEfpL85F2ij5QIOt2OmjvsE2AUNjbIrhnd3MDCbMne92BaVKt12vIWOMlz1OomCChnbVL2Fuj5+a6JQONCgW0d6mq1tnfRBzS522pUPbp2lzPg8rGr2IdFCCTBywpNATygjGXxNAOfWZiahYN9p1TSWiG0dTkBDxmig2GEXJ+KFuDCAP2PvMqdUw0qExfcTREnU6zKyvLWDhCp+j55WVk6/TcHty7i3ubdGrd2dnEPnvBaeHBeoKKcpbnsBjeUAqmKfOpk6dNpWh3d980IQMRtraI3RiFPdgu3eX+/n3TKD4zvYgaX3+QJvDZNbxVakLbVD1J0w4C1rtpDzpIeL0p2zZM1y1G7yoIAiT8/fXaxNgEgmqlDCgmluSW8eYS2kadWUiWU4LiCtP29jryjJ7xxoNdbDygdd8vSzDShevXLqO7T1WaOIqNXUmW54Y550gXccxQmhKI+edZluKtt2lPmvy9Oj796vN0beEdlDyqhPTbPeOc/qlf+e8ee4/9QYASw6S2pRGxrlzdH/n45aUy7EIwN03R4nF3ZmEOZbbbaf9gHX/4Z38AAJi3IjzFdii/+su/gPwsCbMO/QomNVWi2vUafvMbvwsAeP3NH8K3C/f5OvZZs2h55YQRAFyYW4Bmhuju3i5WmlOPvbciPKeMr/0KtSB8/rXPYHGGmqgdyzbEoX6vbxhhWmvjc2lZFlxu6u72eqhVuOLkl43tSBAMISu8x2TZQzo+yrAGtYG60iwbiZ7aDjRXh4Jc4+0bJJ5q+z7i7NHj9JEzVUgNj42/tM5NWQtSGOqp7Zdx7Tbh92987/tYv0WeJ1Gviyp7uB9bnoHr0GT7znf+FPCp3NxsTeLuGg3wcrmKuVnaiLe2dxEm9CBd1xoZhgqNnMu7lz96H9c+JjjMdj2UGB8ueT58xhLHCS000ogWr/f+7C/wEpueHfc9oE4DxE09uFyKqz+IsftvvkXXHycIuSegNxgYdsLNOID7NLFB/umXfxG/99v/AgDQfn8NdV3g24nBM7VQ0LxB2gpQjC1v6QR9ZpJ85qVP49zJwiRS4kkyghAJBFNYh1hAEjH9VWijomtDQKSFgFaKtMAWrDIsmw1MIWHN04Jx3Gui22YVY6ePZ18lgUSUT8Ge4D4At4Kck0OVaQO3HNzYiv/mf8F4p4x9d6QDeOkqwaH/5t/9C3zlywSZrq6ew6eZXXXi9BLWbpNi6f37d3H3Do3ZMBoas7rTp85B8fVmuTbJQBxHBwwfgeefpV6O73xvG3c44dVCGu8hqNwkiZYW0JyMp7lGt8eTPMpR541pnPBKFbzw4ssAgKWjJ2ExbAFpw+y3atQF1e/1kDN1tlH2MewQFBUOeujz4ri1tWMYOlIKZDnj/ZY0asm2YxvvJ1sCnl0cLDRuXfuIv7OD9h4lV64NA8/qLIP1BEXk7e1tBAHNxXq9QdIAALTtQvPmIYVEi5OcY6fPmDVmf2ud2URkYFswrXa397DPrI8bO0MMI5pn07Uqlifo861aGZoX7l6/jwFvnMouI2J4eXd3F1sb9K7b7V20O/T5SmkKfukJ1puHlN0FJG9Ie9v34bJ6ehwGEPzzKNzH7g4dMkr+AaVrS6PPLNIo6KHeoGfiVCuoVem5VcoTmFsmyGwwDE0PjRDSwAZSxSgK/Vpn0HzYqlQqmOC+PNu2x0548mxohBJqlQp8nxKePCcfNAAIgyFSbsSJsxSae4u2Nu+g3aZ7OnlyFctH6IA36A6RM4V80BsiZpZhrhKUKzQPHMtFm1myQRLD9+nnE8vzCLlH7/Vvv46Ix8XTZ6chKpxcZwlU0h/r/uhveUhYmiRBirkJhk+zEBvcS9oYBrh5k/anlypTWDhCCUP3w7dx8bvEnnzryruQffaYrFawNE29j9fsIU4yXDk3tYJ1Zmn91m//b/jRj98HAMw05lGus2xAb4Aphp0n5+cM2zm3pWGitVotbLJX5TjxC1/6Ev7pr/86AMC1LSg+DMVpgoJW6+QwzNEgCA0c3ZqoATx+r1+7gVaLxubKyhyqDdpf9zu7yDkxFpaLJKc5msQJAoadd1RsDqDDMDLJpOWUEHDqMshjDHv0t2byKhL1aCbaIaR1GIdxGIdxGIdxGD/z8cgKTxxHJmtzHNsISmWZBlffcPHjS/j+mwT9bD9YRxZQiX+mXsGxI5R1Vqoa0/OU6R898woiFthfu7UFnVG2uLPdxjYzvGzPHvl4xCmmJ+mkcebUacyy+J7nOPDdoiHPNXoWtm3DfYISsxYwehPx/Q188CM6tQrfRcyn9DROkHLX3s6DO+hxY1TNdpF3Wc8ijTHkUvvVvR1c+DHBdmc/9Tkcf5Yad4NLd5BFLKalcxRaXkoKlArdAsdFxE1eHSSYZLaG9D2cfPlF8/knqYBEzixCdiR2dApzHtVyVErRVGmimx910As90sbRFqD5pOU1l/DKP/wfAAC33/0Wci7Nz7YWMczpdBXHMYQoGCkH/KV+Sp6tlDICc2J8JASeC2h2If/he9/D1TskyPXaq1/C8+c/Rdc1vYTnXngNAPDic59BnheMwxSlMo1H368jjAqfmxQBl7mjcGia9/v9Phw+vdRrJYTcCCilA58h2QwZNOulOJaAFvTElfBw7x5/ZzBArTy+2Nn0zAxWv/Z1+h7pImSnestxYfG8zFUOySf0waBj2D0LCzPwWX/k1rWP0e32+DMD2Mz2CgLPzAPLd5BxBS6FMD5KWiu4PBgcWyLmKuydm9fM8yyVHEir8CnTj3UvPhhrt64h4mbsSqWClVUq60/OLJqm5UqpgrNnqcI2NzOL65eoyXX3wR3YBXMGCiWuQMdxih77OfXCFAM+mefpHrrTNE6PTvpYnqNq7t2dNjaZCXX87HnTDFqptlAq01wfDIfoMuvUdirwML6vnee5yLNi0gn0OlSOD4IOXC73SzliswRhH0LQM3HdkQAjACg+zTqOjSRh65u9Nobt+/ztLiRPuqnpaQPznVw9iiCgsdrvtZEz3NLt7WHI7BrbEvAKF3idQ2O8CdnZ3zRO1t3OtvGoKlWq2Oe1Ugs5MjUDiSLS3wQsXnhc10GjSZWTVqMGuUxzaOVIYFiSSgr4LDQntYUL75Coa7i5icItY3amiTOnidzy/rvvYp2JJS+/dBR+4acnJZwn0OEZBgFcj5+NUkiZjRUOh5AMdU6kIV6aorV7YrqBN7/xDQDA5s2rmOGG+mOxQrVF7+E9pZH+/KfpOVy/iX3eC3/wYBO/8zu/DwC4cusu5tkzy5Fl9PjZ1iemMMkabdVG1bClLMuG4xQVMBtVPf6+6Dg23njjDQDAqeOriJmBOuj3EfEedu/OXayzJtnv/M438MorVIE+8/RJzDIEtrKyYvIGISR8Rnccp4uE7VOEJU1l2rIkHGa6lSsORD6yWwnTogUkRsibZ6JtJLxWRU4Zj3NAeXTCE2UQ3MtRrjjG90ULgSs8cL79nTfQYYzZTvo4xgZ+K4vzKLOHyemzx7FwlF9UpY5rt2mS31nbgcODvWTZUFziqtdLWFykFzg7O4djR6knY2Z6xlBeldIQRT8BpGF15Co3HdzjhIY2PRbzs3P4+A6V5pVdgldhs8kko9Z/AENLI+La2n6/ixI/n6olkfAs+/xXfwXnWehte30DHotgxZ6HhM0shevCZ5Vmt16FZsyzH0bYZ1HH3SDCq7M0cPYHfUi/UGJ18CQpj9QRVKGonNvGwC23NIqMhzzKWFhS29C8ICmp4CSctOgMomBg5ClEmRLR5bNfxZV3/x39POtjcfkr9LtOC1HhDyQkLXT81AWXt6nEX/x0xNI5SFcfJ2LGlYUAdvYJrvoPf/N7+PGPXwcAnD72NFYXyPTu5LGnsbpCm2m53IJb0IGTDBGbDsZxhDCkjTKKYmwXdOY0NRBYyS+ZZxZFMcCeWVGskOVMefWrhvIqtYJiD7QwGiCJxmdNlBsVWMY1E/D5fQoYAghyywG43N/euY+AKcELx0+h5tPn337nLcQp4+44yMLMjXqw0tIoFUuhDfsMwkbK781WlhHI9iwNp0Jjszu0kRcHDiGN6u84EQz2DXTS64XY5Ap8qVxCuUSbR2tiAssLtJZE/R5279M6tLO1gT5T7KulMpbnqXTu15qocGJp7+eIGL5uTlQMnKdVhhrDUufOHEPO/RnhoIt6jf7u0vIJRDknXdWPkG8XvVsJMjW+0nKWa7OOWpZthOqE0kgLw8tco1iaLVs+RN+1jSmjhRrT6pWKjTCjI4VhsQEW1m9R/0q9/CpmJiipI08juq8kjJFzT4yfpRiwIKGQAkoVqsEjqPlxEQQdIx6YRBGqfGATjgOHlZuVHM1roQUUC9k50kaLWXed/RBpwn1GvjCJfGvSR5kNfv1qGSXuEbIsG7Umff8P3nwbt9mw+eb1j3HyBN33a6+9iO9/h32mBn3YDkOgkBD2+GCHSHOz31ieBcul3z0mGpjp0P4xvP4Bwh69z7++eQ1vMoxcVgonWTJh6egi6s9Si8D5RMM/Rm0QtbMv4Q/eJAbWv/6jPwGG9LdmlpdQYT+y/a0uZuZpDVs6eRzT3N/Z3d5Ge4P242ZrAhnPxQgW1BOwCS9cuICPL9Hh/zOvvIRnzhHElmUZrlwmgcQwjNBiyYrvvfE9vPnm9+g5HFvBKh9Wzp17HmfP0n2Vyx4qzIys1SYw7PPhMgmR8OFSSMB2mXFY8pENC2ZtGeGQ2mIypaELmjK02TOklPDcRystH0Jah3EYh3EYh3EYh/EzH4+pcdlQqhBfoy5rANi99wAXGMYabmygxke9I3PzWGxRxaY/CDHHlgrHnnoOfDjCIEixvU2Z2vbOPorjS7PVxImjJO72/LNnMc8aL5YlkcT0/Vk26gSn0m5ROlfGM0aKAzDNWCGMbkVaqeCvuBGwG+9ipkww0xGnjGN871YSQbC/h5OkBqYJoTHgezkyN4tjT58HAHxw+RJisMfP1DQ8PsFo38KQ7+VuOMTWDp+KhIC/TCfYu9tdfPAeSYb/+j/7ZwBDeNCkcTJuSG2Zikp2MMVVIx8xCQFI7rLXEkoxZGLHaFj7/Pnc+FHleY6AGzdV1sGRJlU02le/i3SPGmQXT38GFe7QjzIf2qF/58IzEMXBZkghGEIrbnLMW/Q8F2FUsAUSSK8oE6XY3Oamuv09fPw+aSO9PfFjnDpNVcOXX3wNC/M0Tu0DXlFhGGLAIpRRFGG/Q88gGAaYnqbTVKlcMTBEZ38PUUDPOE41Iq7k1aojxpAdJQDDlRCOqbSNE/VGE8X5JElS0zQpIFBID0qloFg7JRkGkBb9rerkAppV1raYW8HHV0hLKcty8/57vb5pZC1Ly9hhpHECZRViaiS0R/8xeneZFrDZcmLuyAwq7F21v99DfarwfxsjtMKIfyjQZS2P2zeuY2KCKp2+rRGEBTMnNCp0vW4fe3s0h3Z1B7vcwFqtVpEwbF4r+yjky2anW6hypQ6WjT43Nqs4wUSF1jApNFyuSszNzQO8TiwsXMSte1Tx0xDQT8C2UyDRNQCQWpt5fNBdWwo98sbSCkV/vwVhIMj0QO1eqZFukbS0qc4KAaQZVRPurF3C1PQqAMArV6ALl3HloczkjCjLAEHVAde2oA3sm5vK7uNiOOgahlSjUUdeeEhlmWGiQhp+B3QqjC7RZGsCHj/j7c0O3voJaTudO7uKMlfgCn8tAKhIH5r3HqUzzC1SteGXfvmL+OA9qkJc/vBDXL1MzcOfe+2LmOKWiP4wQ3O6xs9PAE/geN/phCPfM7eN2ZiqfbOTU/j4ChEjZrsR3r9B7MamI/FFfolN18HsM9TiYE3PgIsWqJw6iQvbBGP9wX/4Bi5epuqK1g6mZwhWtR0Hd9eI0ba0sIqj7CtZaTaQFbBaFCLiNWAwHKCwBNFKmmbjceL+/ft46Tm6zosXL6JSpvsNggDXbxAx6ZWXP42kIDJZEhcvEnFk876N9/jff/CHf4Q5htteeP4FfJqd1s+ffwqOw5pIwy5kPhrvRUP7IAzhCVpfM23BZpHGyVkXXkzjKtcZoGmuR1FsmHo/LR7N0hJixKYRI4bAu2+/hf27dNOrEw089wwZN1ZLLm5fJ9PJo2eewTPs6+NUprDHL/MnFz7GpUsksBTHCRZmaLI999x5PHOOBP3qtZIppyo1mvB5no7MJfVBppIwwkVKjZKfcUJqYcSK2paF+VliNey0N3FpnzbuNduGv0z08FkIQ4ucmJg0WKtWOVJe9K9t3UeDMc/llUVEA3qxm66DawHTfXd7aHMZr53E6PFLdhp1RJs0qBPbwjb3E0zNTZnNRijgwL7w+NCjzengs3k42RDmS7VQkFwy9OIQTUVlcZFtw2LYJkkSlDhhy5WAY9N9NZplDCN6v1ffuooI9Ex2wibOvfJfAgBas6eg809eYA7e1tjMkHwkHgjhIOPFOUqAqqRJYmvflDO3dm+gO6BrXF+/ieNHqc/nK1/+KiSLhfX7fWxuEvQax7GB1waDAYbMTGg1J1Bh2DOONo2hYGcQGPHFT714Hqvst/Xdv7sAYbOpZb2M9AmEwAb9wEBaaZqO3qccQR6OBPrcgxH0h/A8SthrjVnMLdCic+75V3DjMjE9th5kCAsBQzGaQ3EcQyub7ysyasmuC9g2Q5HIkfNmk4syFo/QPb7yuS/D5bL7cBhAheOzXyw5QqOFoDkFAN39XXS5XyGJAvQD8tarVTQEJwY61aY3ph8MscWf90oleLywOn4drRrdS9lzYBdKshmwzzIFnUEfzT49w8bUEpaOU6KV5Qrzc3QQeenFT+HDj0lOQ8F+iEL/uCD1WPp8GIajZNgvfSJzUUphknDokfhkhsz0aniehyQdqYgL3tjyLDPrw/bOGnbYDLJSm4bkBKnkNTDHzNRO+z4cixIO17YMgzNNUwyC8d6jkBZKzD4Utg1VCMRpbdZ0KSxIA8kKc8jxHBe2HJlsXrlMe0y/38fnPkv9IY1yDWWWJmlNTMFiyRISbKXvbzYmMDFJic10q4b33yZpip2dPUhmb928uwfJwoNxlKFaHV8RfHe4i7k5OvSstnvYY4jnwsoM/uwNgtBfdidhl+gen9UuJs7Q/uHOTqP1zM/RPR5fxk1WKP/Gm3+HP/0+GUVHSYIaHz6mJyfQahKEd2ttDbUWjUdZ9iHYiHhz456B3+fm5lBjxWmtFPKUPhPHGZLk0cnAwajX61i7swYAyJMQ37hF97izs4Myt6Hs7uwjYBHTo8ePI2dW6/zsHCKG0y9dvYoHLIB66+ZtvPUW7SULS3MGDquXa1g9QqKFZb+MLpvMaki0GjSWMlgIijEubdTrLE8jcxTI6/XrNwxT86fFIaR1GIdxGIdxGIdxGD/z8cgKj2VbpoCQpRkStkWYmZ3B1/8RMUZWjhyhci/I4mH1OWJQ3H3wANvsu7TTi/GTnxCb4vaddZRKlLWdOXEMLzxLwnSzc7OmjDsYDA1jBALGal7AQsKCh3mmodRIjrqIcasCRQg9grT8hUWcZtGxufl5rG1Q9h31+9DcPKUc15SeEyhjq1GxfSR8Hc3pJvZjuncNYZgP93odxKDvCRtluOwpdv74CTz3WbJ4eOrZ8yR4AsBOckjOmk+cPGrehXwyzI6fSXF0Hh2j//5zE3nRbKqQMRPJzTPEPWI9tbwc3T2qwQbBEDF3y+4FuWkom55sIGQp9x9fvo4fX6Hf1c485s/TmGlKCZYdghBi9M40zLVpPX7veRqnyJglZtsCcdHkqYGyUwxxCxk30Ao/gVOl0/G97VvwfDpBZYiwtUPv7e76PVy9SiXpMBxgeooqkUEYYG2NTp5Hjp9Ck/VPlBJotxlS6XQRRPQ8ytUSPvs5Op1+dO0KdGFXEQdwn6BRMks1ediB36SBw3JzipeQUNwEG8chymUqBzcaNTgui5RVyjh1ipq3Z6dauHuXqokzs9Oo1+jk1uv10GMmVxQEqNYKbZYSbKcQzUtNRaJUqeLYaZr30wvLAOs2LSyVMNh5MPY9OvbIu4/0N4xwk2l+jpIYnT69oynXwSRroHRnZtDjOZpqjTSkKpzjeaiWqcIzHAwQ8Zpxt7OFdI+uc3myhQHr/0ThEBPM6qpPfITG7AI/t1nw1+PFl17E33zvTQDAnbsPnsijSEppKjyVSsVA4lLKh8UWD8zRT6rOSmkZIVgpxAExQ2W+hyrC9Du7e7vwqkxWQBU26ws161V0uZLdae8ZJ+w8BSx+15ZtGbjzcRGFowpWnmUI0kK/ZcTWsywLnk1rbsnxkPG+opTASRZ+3dzt4uxZqvhvbN7DkHWSjh1ZQplZlV65OhLCzDSkLJy7Y2hFf7fRLGOKGXgnzxyHWycrhCvXruLYCXq3aQq4hcrhGCEeXMPEFWrpiEs+dIPmWf/mA3S47eD3hxv45YRJKWfPoHJsFQDQqU7jX75Ogq3rf5bivY8JZlehxArvPZvtTczO054ah0Ncv0UwWbM+i3qNqjfHzj4FhytaKxOL8LnqFaUZ2u02/26ENCosdCwEw2jse5yfn0fMgrwVv2XsYpaWlgzzNolHLQ53bq1hgis29+6uo9ehtbDVaqCA1VQusb1FiIiwLNy6QVVSS2tc44pcqzmBbo/9JuMc97oMm2fKWOhkuQUtacz6ZQmPx2m9XjdaVj8tHg1pQRjqmNY5qswq+uznPw9R/PEsQ8z/9idq8AUlMztBH/cfECRw9cpNU/o6c2IVJ09SeW91aR5lXoizNDLlWi2oDwIgPLuYzEJYpoyrbIE0KWCvUae21sowT8YKAVhc5vzH//x/xDf/z38FAPiLb34TNk+C2aYPi8vK/TyHYPXY9qCDKqvfpr4HZ4Ze+PkzZ1FnZtnrf/mXmGVafTw/gXPPkhHcMy+9gOdfJJr5yokTqNXq5pKKrezgdpipfNS3859SlyvyHa0/kW9BJm/0LqQNKMkJHgCbE8Is7yNlWNOybbBHIJIwRKXK9MeShOowM0BMYD+hDbXhS5QYzpFa4acVV0fJzxMkrkrCYcqlk7lwStzXkQ8QatrIfKFQKhgLKkOvSwuoyB3cuk9K2H/1vW/Cd2nivf/ex7h5k+BZz3KwyGV/3/exs0sLytzSqkmEHMfDbpuShDhNUbCxP7xyFV9l0S7H1rAYdsmjHKL8BDRR2zWGoUrB0Nmo36MQaxRo96jXaBB0McVCno26A5upzchCVJnZsjg3gy98geChNM1MCdv1PDgM97T1HgLebOr1GjyPfZrCIQTTj+cX5nH27FkAlFBlDId5tkBqPcF71MrQkpXWo34erUbMJsfG9VuURJ+YOomZaZpbt2/dNWJ9llCweYGuuA4kJ5meBUN/7Q2H6DL0WfVLhr4rrRRDppzv7+1gOCSoq1ydNHTZ6XILJ06uAgBurd1Clo2/WdqWbd6d4zgAr1VCCJPQHITliT1XJDDS/K46MD8Ofl7CMtRcAQsOQ2at1hK8CsEhq8eegsM9KEJF2NkmRlM47MAvDltSGOFNaQkjWvi4KJcapockSwGHIeXm1LRZu13XRYXF8RzLgsUil1mmsbTECXuiUK3TOIWO0dunuaWEQJ8NmDf2erh+4w5fe4pahb7z/HMnMMteVFcuXcNeh8b+j97+CAkr8ea5hXab3nOzMQn5BOahgzu7+PSvfQkA0L+1jt13CaY5AonjNRqPd9wmYoagX09zeCwwePnGBXR4Hbp24xoc7pUUno2QVduna2UMQ0oYIH1U65wMTE5j6Qi3XKytoVRi379mE9HmDj//uoGxPKeMLOb1z/eh8vEhraXFJXh8sPdcGxZn5hraeAOmSY4goGcbhhHmcoL5wigw7y5KQ4Ss7J5EGVLu+Xnq5AlsceuGyHMMWLLgwfo60oSVnIMAzQXqXyqXS5hvsQms46LTp3VOWDm2t+jeJ1qTUPmjN8dDSOswDuMwDuMwDuMwfubjkUfMNE1MGd22XSNApnIFzceIJBlpsyRxCLBmx8riAoYDytRWjixj9Qjx8mdnJ2AOCyozDcl0QuEKzwHXZ4GR8J1lW7DtkdNwocOjlEZcnCqy/MlgLWH+B5FjYcCVCj3dRJ+ZP/0wQYeP7D4EXNZdEL6Lba6ARUEfzQpl4n/719/GsPwDAMBnXnwRmw+orP/f//P/Ca+88goAYGpqypQD6R7+46qU0tqP3qDbAAAgAElEQVSc6CxxAMh6MkQLB7QDoZUaVXgOnCqp1FW4bisIbk62kKHCDAmRAl65aGbNjNt7reoZeX1LusjZB0hkCfSATi12pQnbMLwevt/iGp4MjByFIy1YXOFxLReWVZTuUyiuQiRSwylgUiXMKdiVjrEz+Mvv/Dmk5ArlVge9HpV06+40bG58bTaFGWt7e3s4tkrNdlOTTTMPpNZw+V7X79zHHmu2SFimMioEDCQ7TlQdoMzOx44tYUmel9bIsiPNNC6xs32/t49z50j/4shUxbD0PNeBcOgeZ6dm8cqLxMS4dGMNOwO6r8/94tcxyXLwe9tb+OB9YgpmcYAGs712tx+gzWy8pZWjKDXpJKakBV2IFqYRonj8MrrOFWynsLGwRkQEnRtaT5rneLDFNhbuWTgsQJZm0UiqXqVY4Krq0tIi+ux4vjHcRlZ4L1kCZWZjLR09gUqFKhGdjfsYdKnsHofD0fuSOVKG03c6OwAKt3FAyPEryju7O0bvaHpmGk7BnDlALDgIbR1cI4DRmMnyA1UdeRAKtFAMbq01UhZra7XmsHKc3nV9chqC17PO7jqimE7L5bKEXzDyhDJwkZR6bNju2NHjSLgypLU2UKp3oEKUZZmxPIij0NyjUspYGb3w3DkzdizkxmupP4xxf4Oarz/46BquXV8DQBWeuRl6507Zwb0NOvV/eGUNN9Zo/t1Y6xpLpGq1gj/6w2/TfZd802bxa//t//zYeyyfPIN/+Z3vAwBmEgtbXH1quAqaxRpvBxE6Cb2fuu+iv0ckiazXMWJ6Osng8TP2Si4cj9eqNMbcLDGWpVPFBsNAncEA1mbBorOR8nrW7fbg8fW39zoGCciyjJlaFFE4PknCtWxTVU0zjaRgFkqg2IAynRs1WcsRxrPO9X2zR7mpRKlwV/dSQ3w6f/oYrgkaJ5MTE7h5k+CtmakmBlw1urPVNlDqYNDHTsr3YtkoLPqqjQYWVqgtZrDfRdA74Kj+CfHIhEdKGCaDlBYShpCktExpGGJUXhXSMjcUDgIcPUIv7eyZp0awlMRIjEyjgPfYAJEn8EHKpRYoFMKEkGZR0FoZs0aRS4PdCSFNP8eThuM4mGDsdPGpM0h58vd229hlP5Zw2EdeMFvykQdWZgt429SN/rI6Ax0V96hx+jS9kK9//euj+1LKLOjiocTjQBz42ZMwz/5+qAO/+sikomBU4ABMIoVZgIXlo8osF9sOoZnKazsBXK/wdpKQxSKtlTHd1NKGliO2iVmgxSgbI6XnQkzysVdrwoJEVpTfc2l6DzzpIuVLyZQ2pVULGtIZKfd6kibV3fY9hBEt/tVmDRWGGW0FZKAFdBD2zf1tbd3El75IHmLPPH0SH3xAePzuXtfQfvudLmIeLwtz8yj2MqXSh2CJx8VUzUGVk3EpgYKa4LqWQf+iKMHpUzTW0q9+HedYkXhxfsEkdefOvwBwr5bqbiDsMt0+GOIzn/95AMDnvvhFOJx45HmGl7m/7NLFd7BznyCEsmuZTXNiYhIWyzZEUWLeWpJl6DKjbZzQGubZeq5nNrw8zg+YluZQnLTEUYKIk5ndXg8x++zNz8/g6Clijs5PTeH2GkFgO+1deMoy11yusJml62NxmYUoXQebd2icOFYGzyuo9wN0GC5874MPsN+m57C6MonhcPyEp+KXTL+IVCN1cyktM88eOgxo/RDTqZiXFqShduf5qI8rzWNzKJTSMj1mk5OzsHitSqM+HqyTNEFv7y5CNn51vRGMmGU5LP4eQI19iEyzyJg3J0lq5BmSODS+SEopA5FJIZAbxqZEl6GNXq+LFkMYz55/Bm1WV7743hW8fZEo2+12iCwvTIh9dAL6nj/9izfMRp8lAr5LyYDrOCPz3jBCp01j58jKrDnEjhMTzz2Nv/3XJAy4e+8uLIY6Hc8x61DFqsBiWYhsbwvxkBlGQWaEF0uOixKPBSGBNgtnWo6HzXt0mMj0HqYYTnfKJWMU7JXKqDF7S2uNkJOENB29q4PJcp7nUE/Q6rGzu2sgSCEEbN5rLflTesosCclwUp7no3kMD5bxwbOQWfx8qhU0mU129NgxBJyMPXv2aQyH9O/v/+gC7mxSspcM+4g1PUMtJASzCauNJmZYnDcYBuhHh15ah3EYh3EYh3EYh/H/83hkhSfPc2PPblmjk2QYxibT1FCmEKHSHBGLBLqOizLrd9gHMk2tNCSffnOljYQ9MCrLaoUDEjvCwF5Jkpis1bIscw0CFix2GHdd1zTbjRtFplqr1fAV9is6euo0PuIT+9rabdzfoAarja0H2GPtnV6nQy7pAHrDEGqffo7338NnP/s5AMAPf/AD/OZv/iYAKjEW1//3tTv+cyo4jwstBfTI1cEQfH661o02FTytlKk0CcuF4qY/LR1TxbBtB2XWP4izFBbDSK7roGxcnFuGDfdJfw3FtfCxlYTUxjtVtup1xHxysLSNmOGGIM+QsvaELyxobhgWjgXNNdFBd4Ccr7fu1NEssQ2BbUFoOuGUHQ8Bv2dLJQi4stVtL2BqkqoE586exiluxh8OP4LiKqNWMYIhVQaOLM9ihpspw3gIyx6/wiMtIGZ4Itcj4Tkd58iywh5CYekkCV4effp5aJ5Em90EvR6dmuZXTiBnSO7GhS3cW6dKhYSDk8ep0d6SNiKeQpnwUJsi2O7ZT/lYu0Iw0O2Pc/SZLWVJCxaPI9dxkPMYsbSFKXaAHieEZZsGeWnbsDJeJ4Qy5XJbKOTMHrl95wF0Qv++vx+hwg2jK6dfwLBGzZ039jsY2FSpE5UWKg7d2NTEFHxmpe31u5joc5Wj3iRBIwCVkobjFp5iIbZ26VndvPG+aQJfmKviweb4WkPEZKF72dvbM+vA5OSkqfimaQrHLsQeLeBAxaeAw7Q6eNK2yFIH5IFVwEUEbdD3b96/C7fE91ipYuMeNeRLPTDzOwijkaO6tCFMQ7V+qNr8qOgPesarLYpC1PgZe54/Ym/lCvvM4slyZfaAOIyNf1257EPyvEzzHL7xu/Pgc+Xh1Ml52Db9PMkSZOwtJiWwvETvP44SeAyrNRt1tPdojd7fa5s2i5/7+U+jwXov44TMHXz+K7RPrF37EFvMNsoHAWIWqvS0QGWCYPCd3W24/M49vwHJn0myFB3WaxNaGgsaoTxIi95DvVrG3CLNoVQAVdY42u/1sdmmdcVSGorXBg1pKjNBEDxUHRRPoBcVZ6khJUjLKszPodVo79JafyL8KoQYCWpKwLaKiqNtxvjVazdw9y6xoJ99/iWkPAayVGGiQZWfsufi7nWqREZZgoIlk0NAMstveXXVtFNUG1WojUdXsR4jPGgZBcc8VyOvHTUqrWZZaujkSo/8eKCFKVM5dvYQTjsq2R6AsTIBwZCHPsBJVsgNRi6lNA/MsiwzgaSQptcoyz5Z0G6cEEJggsuEn3r5FZxjRcy9zj72uF+h3d5Fd0ALXLfXM6qvUZSZ/gBkGVoN2gi/8LnPY55hsp+W1Px/mewAIPy7SEoxMvHUB9gdQhzUuB2lGrlSyLgU7kobrlcMXgdRVFDB01GPkIbp72rUa6hywlNqTMLizUypAyVyASPqR+99dA3jNvUkcWJYRdVSDbspLaYqteCwwFnDKRnmTi5yAytY8Iz5oy99uMzwCoIBHC5JN92WEd/zPQ8Dxcqe/RB37xBFPQ57AFNhm1UX07PU01KtVdDZJ9x9bn4Rr36KmHlpGqHHnkPjRHsQwOJJriARM9shzhSkVXidKWjN1HuMEkkAyNIRgynm+VefnITFNHbXriLgqvva+jYSFoNLYQE5faedx8htWnAjXUJtmiDran0CvX1mTdg+LFafdmwXlVpr7HukxZP76aLYSFAcGBZQeY5un6CIH174CD6jLnZpFs0ZKv1vJbO4+YCuoSnqqDWJPSKnJawh9TjVWjVI9uzZ2t3CzTVKZmZm5hBzX5C0yohTerZ7WxF+8g71YVy6vGHuUVgpwnj8xJXGPX2/7/ujQ57WZi56nveQdMRDc5T/FPXBMMxgjVoMpDWax9A5el263zzL4BS+XUEbtsUbZC4RhSwH0hv5welcGQaq1uOLue7v7xsRPIAOPcW9FrBLe6+DiA+lfqVqeo4cy8bkJB0IJidbqLChcnc4oH4RAMePHcES+yy6rgffCM3lUCyEaVsuHJ4rSZqZ3tMsTdHpdvh5KAOpSEsjy8f3Q7v0+veRsVnrylOnMH+clNqvvP8+8luk1G9ZQJdhLL86iRJD/mGYIWc/N+qBoWtzpA3JY82RJWh+bgurK3A52WtWq0b0tFwuozVJyWQaRgiHhdFxjH6fDgFK5eCcBVmSIkvG3xvJ+Jl7cjzXHJLtA5DWwTEL4KHxaH4qR3uzEKO+vMtXrmL9HhUR3nrnAm6xN+fi7BymTtEYaDRqiFi4NFMZclH0mErw9EPV91Dmw0EWBYiCR0Poh5DWYRzGYRzGYRzGYfzMx6OFB61ReVopGM8YlSu43shfJ+cTEUFXI7aAPOCXdLBB9/9l781i7Mi2K7F1Tgx3HvLmRCaTyeRYLLLm6T29eeh6asndDUmWGk8G3C2jIahhtP/80YCsDwGW3bANPH8IsCVYH+1utSBr1pta03t6Vaq5iqwqFlksjkky58ybeed7YzrHH3vHiUhWkryULAEmYn9UBW/GjXvOiRM79l5rD0kwlTCZVpGKTIlxy7KNVQid4mCQUDC+7yMpTBaZsWloY9GPI3cX9hIqqWlSZq++NFfAEe6+jMRBGztbak+Abup3/6FERdp4UQKpIHMAdqq4myn6JwS0iNckhMOwsRQBBKMJEkDEvqQXBnCCOMAxjyHXkBgFCh1GvSadHFzEVGYK4VHaUC90lFBp48b0RtJCNOJ52CEEZy9UVQGaMyUs2CbbLFIhwFldhXzZ7PFA+VDsZeWdOgTzgFvruyhyUK5TcFEvE3rX6/bwH3/7P9BxvwvFfYtOnZhBlXvKjbTC1ZuX+fNjeOFp6jr8xquvmWyNcWTXU5CMeCiV1MLQGqZkfyrhkM+L6cEIQRCfE2GXoRzhFmBL7lVj1dBmgDLoD6ARIw8RojjJQIUYsddqVQ5g7hAVMKxMzCDi2hmRD4CpusCOoB6iW7qKQoMORFGYYnKkyTLzQ4Ue98u5trKN6QYhSIenD6ATEnW1tGljt0o0nJX3YJUJbQtnJqE2qYibzkWocXftte1NbMWtKMoNaIuQhULlEK7dJETgR+eu4ZVXqbdTrx+gWiO6QloWxF2ZVA+S+NEvFouGfo+iyNwvKaXJdJOuu0c/SUMbKEMpFovFVHFWJH24oExH+EgEiIKYehMQjPAoJIUQq9UJM55hf4hKnuaoVdL/60HS6/YoSweEhsY9vjxvZH5ncnISPtNl+XIZoZd0jO8xtZhun1KulFE2tYtcg+J7nmcouHzeMQkkWiX9ubTSZgwAcPAgBbhqLQxaMhz2USyMV2cIAFY/fg/ekBCeq+e/Dylj9LcWv/6Ico5ryVkKrW4cfuHDBp1vpRgLgQgu14OTrsTi8WM8ryJ2m/RbgefD5vvc3WlhY0jZauVaFf04GyvUJmlHqBCdDiN8ng/rIbql+1EIKx5bFJmAc6lhqLE9RWOBPUikOZY2Ih6PFBrgoPFiuYqpGULq3nrnPWxvE4Ny8uQJNFo05lavZWgsx5LIxS1WLMf0Yrx14xq2uQbRyPfRYAr1XvKAwoNWKvtJGCVrWbY5dp0cVExXQe3hj63Uwux7fSHMwy+UNNH6YRiahYx0ZIyfdDXSdHYPhIDjuOach622fNekSdL0ikKS6qTNf/a8XHQEEyejpIaK04Yh/0GNm/3EEjZUTMIKYapmQmtjcFpAUvhRC6OVHSfpe2PZCjbzw2EUQppq2NpQREFoQ3EmzMDzTUu+Yr4Al7lcSAvWnkCi+DoCIt6SSsEasy4fpeXySzYI4XHlVls6sDiaH0pBcLaA47oYctnc3UEHRebFFQTi8K80pBtpYdLAl3c3jfJXjoPNNTLuLMtCebLMvwtTaXuoNIZNGttHly+huUUPs0YPc4fq400QgA/XOBBIx82pKNmyIr0lk55vlpAm01HpCG6VjATpCOxwirdTLkI7rIhdB5aIG1wGkKLEc5TQ06SkDi6eNBXHbSuVPRnCVAD2g8AU1htHFBQ0Z36pCNDxAyWB+JmLlEbAn+fcIqYPUdxUvV5Bc0Abpje0sBNxZWmt0A84267gwnWIXmwN1zDZIN02NzeP196hZoehLGOGU9SXN3p4943vAQA+WFpHa5fudT6fg8XF74Kgh3J5/PiP9MsgnUUjRJIN6XnenrjH9DmxIdRqtTAa0T5vNBqJsQSN5P2uIBFnW2qTYm9Dmqw3LQXy+djpVKYnnetYUFyw0bJgjJUHyfT0rFHLFEvCpSsqVcSEQq87QCmmiEslCK4SbEHgAGfcWFaig7QlDPVDa8fGUj5n9qAQ6VqlImVIKir2CMC27dS7QZsimpVqyby3xhFLBcjx7bGcunGetFKGVkvGCqgoZ+IBLasIKeh3bdeFw0ZOpVbH5CRRr2EUmVim4XBoit4OhkNIjr9zHAcO09rdZtPsFxWp2N/AYDhMMrZsm4L/xp2jbZsYHp2ay56ytanQrrupV1MsExKwkpzbeP/m8gVTTXo4HJpY4Q8/vohPrnMGYaeFA4fpHEdacGJn23YRiThERsBig2qiVEK9lhTw3U8ySiuTTDLJJJNMMnnkRfyd0JBMMskkk0wyySST/x9IhvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8shLZvBkkkkmmWSSSSaPvGQGTyaZZJJJJplk8siLfb8//vj/8F0ttKZ/CAEhNR/qMS+vP3WotTafaq0Bvr5SCvFftNbxxwA0lDJfNt9VStH3AWilUtdU0Hz+m7/+C+JBI/zH/8XXtFIhACCMIrRaXZ6jhUKlDACoT07i+s2b/AMKQtBlpQRim9ERQNFWfB2NicYEAGDYH6C524uHn1oTHV8ABceC1vTd/tBDFE9eWggCGlvg+Zg+MAUAODR/CFLSGL79u9974Bx//f/4381qSikhhWWO47kIISDlp+1fISSA5Jx7idaf3hPpz+ie6n2Pk3MU0nsm/r1f+sX/9r5z/PX/7X/RWtH6lYpFRFEEAOgPBgh8DwAQ+gFC/jzSgGXRGkxOzmJ+YQEAMBoNceHCBQDA/JEFPPHEEwCAD8+/DxXRfVA6xAcffAAAyBdLWDiyCAA4dGgeg8GArzlp1rLb66ExUQcAXL7wARybfndqahJ8C/HNf/WvH3gPz5+7oIMgAEBjd21ap5xrwXEcAIBt23AceqTzuRxsm44jpaAixWtqQfD9tCwLbi4HAJCuhJA2n2Obc8JQm/UMwxCj0YjWdjiCF9A1gyCA7/sAAN/3EUU0ziiKEI/5a1/+7APn+MM/+UXtlmcAACubId774AYAYG1zDZWaCwAolfLodWkMd24t42d+5icBACePLcC16ZzRMMDv/N6fAAAajQbm5ycBAIPRFgI14nMEdtfpuRx2hjg6fwAAUHfr0Jru0Se3rqM4QTpg8tAMrq3eAQA4wsJsg675zBNPoee1AADf/Ln/9YFz/Nmf+3ntBH0AwOGDU6jO0d4T+Txsh34XAghZJ0WRj4Km9Q921nDsCI1TVRbg5mmtJEbY3loFAHhDD/0+Xd+PFJaWacxKAadPPwkAsN0yun1ah6E/hOfTsW1LVKs1/t0IvkefF3I5eHx//6df+eUHzVErFe+1By7H30r20zV/l9/TWqd13wMv8qu/+qs61h+FQsHoEsuyzHUsyzLjlFKaZ9G27T06Nz7WWiNet7uP4+dPKWWepyAIzDlRFJlz0t+NosgcSymNnvjlX37gPcS/+3f/s37+hWd5zIDnkZ74mzc/xhPPfYGub9fhhzRfqTw0qjSGYwcbsEHHVs4H2F6wbWnec5YCLBnvdwHL4veoYyHi9/f/9e9/B8urOwCAiclplMsF+t3AQ47XfH19Fc0m7fEXn3kGbp507S/+4r/Yd44ZwpNJJplkkkkmmTzycl+Ex3Ecg8AIIQAR8V8SlANC7AFyjAhhvnsvizy2PuNz7kYE4nPuhQzstYjjzxW0HBeBAnqDIYBkHIRo0Hzz+TwAoFKtJkhUFCWWuxCQvA6BtDBi61VAotXq8PkKtkXLrLQC2FsTQiDi87v9oUG0XMeBy552uzdAxJ9blo28Sx6s1ApSWWPPkTwJOiaER6Y+/zR6s+cYAtjnnLRorR/au0p7Nsm9Nj+1Z2wPEtu2jecQqgg2ezITjQZ67TYAYBj10e+R59vp99Fp0/3xvBBnnnkGAFDwPbgFuueDwcB4a7MHZ3D12hUAQLlcNud0ex30enT9MJwx4wmCAHNzc2YeU1PTAIDKxCTWl5cAALm8QClfGmt+sex3j4QQe56J2DvyQ4WI95plWbB4TaS0E49RCGieo7Qs7Ofcpm+B1tp4YrZlww8DM4ZkPHvPfxj54NxN7AyuAgCuLXVwcOE4AGB6dho3lj4BANTrNTz91PMAgFMnz6JWneA5SpTK9HzYtsTzzz/J6wBcu3kdAFCoWPADGuAHb1/FZ598GgDws1//HKYrdC9sX0Gz53nmzGO4yN/VroN2i5CcI/MLxlvOuS4qjYWx5ygcF9MHaMzlRg027yUhAYchP9tW8D1CCzU8hD3aq8Gog60mja237SMSWwCAYjmHHCN75doUhEve+MeXL2Lp1m0AwNzcAqSk9Qm1hHDonIIlITShN1IKKEbnarU6hkOaozcaQqd09QPn+PeE7Nwtfxu9s5887DWKxeIeJCeNjO+PXCfvqjAMzflphP1uJGfPM51CWNPnxNdUSpnPgyBAGIafGrOUcl8E/15SqVTNHvf9AYajIQDgxRdP4eyzRwEAq9s++kP63XLBxfws7a9qXsMV9F2FgmEpioWSme9oMIDNKM3I8xDxmO2cC5ffl4fm5uD7NOZr12/h4EFCN48sHESvTchPo15FzjkMACgUSrizsnLfeY1v8EAYfagF7npRyuQP+wgZI/EGEOb4XpRH+m90YxMaKPlcm5fc3mP1UIp2a2vbGBv5fA75HCuFMEKvT5B3rtNBPkeKSaso2bAAwFSBH4Xw+MZCS0gvWRIeGoRQEIiNJcCyaL1cpwDFa6eVgmszRSElQp8hTKGxtkkKLlQaKhx/jncbMPtBqlKkDBv649jXx13nP+yLLhaCgaPUJ+Ndp1wuIwhIadu2bWDfXC6H4gEyPDqdHnKlCgDAv3ML62trAIDW7q5Zg0KhgMlJoiqGw6GhaYqlolEiw+Fwz3qG/NL3fR++H5rjeAzeaGTWw/N8s08HgwHydm6s+dHvhEbB2baN/XRXmgqm7RFTrxYEKxEtpKGriF6mC2nzaWw48bG6m37ka1pyj+KOlaNSyrwctaX3ODUPkrNnnsA7H14DAAgxhGPTM3fj5iVM1Ylqee75pzE7TdRuqzXE1hYpvrkDDeRcMlo21lbQZoN27tARTDVIUZ57/x2UiwR5v/zFn8R/+ZM/AQCYLRURsVEhlcaQ712+Pompg4sAgM32NgTvzZ3WLoZd0g2b29tYqBwbe44RNOwi7UNRqECx81GwHeQE/e7K9YvwA6LWD0xVUGZ9YE1NQDLtFWyvYmWHoHyrVMfpk6cAAH7k4fbKLQDAm2+/hU6HDKcDM4fhjWg/u+W8oR0lItRLRQBE18bOoj/smx0R+AG6nc7Yc/yHEsuysLm5CQDodrs4fpwM5IfVP3rP7n+wOI6zrw4VQuyht/aTu5+H/Rz4NBWV/k5aB+x1FPcaP/d65mKjaBzRWhln3rJs5HIcBmFZKLl0nVOHXUOVS2gUczSe4bCHQpGe152tXSzdpH3qexE81pEhQkgrps1DYxQpaWHAevevfvAqAp+uv7Pdwo2r5HxEn3kWUxO0Z4eDLo4dOwEA+PpXv4gfvvI3951XRmllkkkmmWSSSSaPvNwf4bFt43FrKVN0Q+Il0v/uj/AgbXFr7Akw3hvMnJyXpqiSoOVUYLNSBhVRSkNxoKFSD+dVCi0gZAKt2QzZKw2MRgTTbKxuYNAf8tyRQkVgvNwgCDAajswcpWLPWST0n21L5FyLT0l51AqIA6dVpKHtmH8SxqOTEOBTsLG29VBezB7IVuz9PKEmZYLgAWZ0hOzxmqS+LtLH4m7ULoXI7flGfP5eLywZn0a8lwgJGc/ryhVKGHIwretKVMrkXXR7fVSrhBJMTs2gWCrzOS6KRToeDEfY3tgAACweO4pSiVCCbreN0YgosFKpiEajAQDY2tpCoUDBc6urayiVyBPf2dlBsUheRxxQDgCOm0ObA+FXVtbQqNHvhoFnUKlx5NNB3/Hnd3mJqZj4+LmJIpUgZ6kATaJv7+Hz7ON5pp/jNFKYhspt2zbogQXroZ7FdmcbR44SGjNUFtpdQm8OTs1gepbWNhz1EDH6sb66hj4/l66lsTBP1NLuTs/Q0f3eEJvLhAKcOnwCzzz9IgDgs899AbMckKy6O1BM1zv5EkSePkcQotKg/XDgwDyOzhE1+fa5t3GRKc5Prl3FkaMnxp5jt7kFzNEcwyBEvkjjdHMl2KN1AIA1aGJng9DcRvEwJNNP1Wod3d1dAEANPtZ92p/dSGK7Q8eB38G1m9d4+AoiRo6hEUS03yo2kKvTvJo7O+gOaQ2LJRcO6yfPD4x3rSMN13XGnuPfp2idhB189NFFvPY6efT1Wt0gPJGKYPMzqEQ6YJvykmMAACAASURBVAGw0nqTg2kVpNFY4wQKpPd7mpZK00bpc+4OTk6ev720fRopSqMx8edKqT3n7HedNMqUDmZ+WNquXKmY6ygFFBiVvHJlBc3NPwcAHDo8i5U7pDvLpSqeee4sAMAPQkNr3L6zgXPvXwQABIGG7/O8bEBY8VolYTGhsDBkVuPw4gmUy/TcLy+tQEYcYjB7ELUqPRO5Xh6FYhUAcOPmCoajT9N5abmvwZNzk+yhwBIQzMtJfd+vGYmXOB1Ss1dxi30/BwBlMrBEYvzQB/x3YagipTTtbACRBKIxxwcAxUIBikfq+dpEjlvSwoANnpzlYG5m0vxWPLZAa5O943oC1SK9CEtF11BXq2vbaPcI/rZCCa1JwRXyeUj+3SiKDB0CAZOlpXUKAlUy4dF1BIxJ98Syh4LcZ++TmZLEZRk4U8GsjxLC3EvLSjSJUip1kzWSzD7zHyilDWwZhmEqKj+5V3ttuCQ77EGiIFCIjQ0hDL3iusl9qFVLJj5hanoWRaa31tfX0dqhF2u4MG8eciFgDJLRSKBarZrz40wlIST6nO0SBIExKoSg3wYAx3awu9vh5bAAzpATSkCp8SFmKURyf7RG/FVtSWgdG4nJMwEloI3hlWTmQcI4DUJImFQxJBlyUgjzeaQ04hxIJVLflQI2Z5yRU8Jzh4YdK3uVxMOMI57ysd4kY6ZSzePUaeLmF48s4s4d4uYvX7mMpZvnAQC1iVlUOJOy023h8mVa50Hfx5lnXgAA/Okf/wCbV8kA+B9/5b/HmTMUrxUEKX3j5CBtdmjyDgTHx7mDNlSXdEBxagbWiPbqU/OHIDXtpb+8+AG6YXfsOe5srGKdMzgrldPm86HvwRuQ0fLkkyfQ6tPeu76u8OUfO0LfvbOEAtN8hXIdJxQZP+eu3cLWWoXn4iCfI2OmWCyjz+Pv9dqwHTZgEMKW9MKo1aZw7drHAIC+Z2F6loy6UAuEHEvoKR+WNb5O/fsQk7gqBd566x0AwLe+9S0cP07xJM3yDm7dIirv8OHDyb0VwmhKoQGhPm3gS2hIKx1ycX+525iJdUba4LEs6560V/qctKRpqZhCTxs/lmXtS3vtF7MD7HUq05li44iQlnFcet0WfEn78e1338WZU/MAAKfk4C//5n0ePHBthYwfy44QRjGlH+HoUaJ8d3d2MdzaBgC4lgPNdFgUJnpQSg2b9WgUBbAdfk/kbZRzTAU7OXCiJkIUsLpG+nt1tYXBsH/feWWUViaZZJJJJplk8sjLfU2+vK2hAvIQbOkCFnmt9wrwulfwl0jVn0kCkGHQIzrWezz8JDgrQXgUpIEnhdKU9QRAqggOW7mhVgjE+DC6tASiOEJTaDQaHNgaKszOkCf27NnjYEACfqgQG6RSWqYm0dLSCjablMVxYLYGadE1250uPF5DIQUinld/MIAwgdaUnRWPJwa+isUcKhYhF67jIufGNVZsFAvjB7ymKUiB1PEeSmtv0LK5jxIG+pUQ0Jw2trO7jSKjGPlKCQH/ln3X/U9ntFns2UT49L1P//9hpVQqIWLqbzjo4wZD+mEYIMfUxoWL70MzCtioT6PDQZiT05OYnaUMq1arhQJnzZRKRXge3bednR10u+TFu65rApKPHFlMeb7afK6Ugs/oUKQi2IxizR2aw84WIRXVgm2QonEkDi0GAKG1QXWgLUhGXoWQEPy5BQGLN5INmVC1wjcokOO45nOpQwBplE6a4zQraSpeCQ0rRoEEkIKWjBdFhNn493Rq5gA+vn4JANAZtbHLgcTXrl/GRO0gAKBQqOHmLa6Hk6+hXidEIuco7DTJe9ShwPoKBaV73R5+5utUN+RAwcFgc5mGb0kIrjnj+31YijxDpTU0aD37zZsI2LsuWgGGTfJge2urmOLkBlsI3Lx1Y+w5HpquYGuDMqfcgoPpEY3fdkvIRRRgHFUEQn4W3jx/EYuHKdA6pxQ6HfJmTx09hKNHCHWU+YO4vE603cTEYwgZmRawMRrSPlxbW0WHKcJytWaQupybw8njRMl1drYQMWLpRwqRxVk6YQD7IXTq/9eSpoG2t3fwm7/5mwCAtdVVTNRoDZpb2/iTP/5jAMAv/dK/NvWloBOvXkBBC9/8y1BR2D9D8V6SRkruRmxi1CZdb+duqiuNCO2XgRxF0R66KkZwLMsyaM/dtXf2o66EEGas6cSCcSSfL0DIOCPTQo91oSUl5jhb6uTJE/g/f/P3AQAfXbiIv/ir79OXpY9SifRoEAC/8Au/AAA4f/483njtdbq+nYMKOPssNd98Po+QdYmTc/HVl/8RAGCn2cLGKtG8hULBzKuQz0PwOzXnFlEs3f+9eF+DZ37KxYmDFLtwZ7ODWzu0WZR0E4zxHunMe15ed6WoJzE80nyu0pSW1lBxDAyS70YaiHj7KqUS6kRpcAgPtA4B8/p9sIxGSeq3lAK5PC+JF2CySsZGo15En7nHfj/CLVYullCYnyWjqFAsYrRCn4eRRI7h/pzrYILjNlzHxcjzzXxj6833Qzj8wlZaweZ1fOLJMyiXiCazZPKCsSxh0lDHEbGHHto/Swswdg2fxfdLCGOYuZbEjdv0svnRf/4uZssEnX/h5a+jxHEJQsPsCa1TETwCpuiebeUTyhJ6333zMGnpo9EIiBVBCIyG9HC++dYbWGtyzEO9ikqZHvhB1zNK5/DCEbhMRS4szKPIMRVzc3PG4FlfXzcGT602gZkZevl2Ol00GvTSdBwbx48RdFtvTGGHU5i3tpqYbMwCAGbnDqLboj0iJR4q086ykbqFESxeS2lLSDtWrEkBL2kLkwUoJO0fALClMNy5bUuzp3Ra4etkv1gyea5DJHtEgpwOOkkATkxjCWPyCCH30NYPktAPUGe6Z/XmHdzZoJiWmYkaDhwgGP25F57EwTlySm4sLSM20nI5F4fm6Jy15XV4bBg8cewgFibp/vY2liCmKWsvEhr1MhtsYQfap/tr50IMiN1Ce3cTRdYHYXsDm0tkqGzevgOH7/uTc4tY5yyUceTIXB07Q3qBfXTxHOZ3DgEAFhZOw6nQPV3bauHgDBlC84Vl3D5HlNNnXnwSiGOZIg85jjerNaZRXKe1igZD+Bx7Y1kuJFOKnU4Pt2+TsVcqN1BvcDo8NA5P02/5OdtkY4WOgxu8/t1OC9NcWuEfUtJxL7F85zvfxsWLFBOSy7nY5HlPTEzgyseXAQA/+uu/xj/6xsvmu5ZMTHAt6IWolELABRfDsAewUVGuP3ieuVwuceRShoRlWXsKDKaNlv0MnnSa+d10VXx+OpYnbfiljai7KbN4DGEYmnGmxzaOjEbD5L2rFGJ7d6oxgVqV9l21VIAKmNL3epCCru84AgFnBGoFgOmtYDQAdFxQ0zJGt2WnCuBCw2H9VMi7KObJsdjVEUZcqkFIjYKkZ1rDgcNZjKVSCXiAYZ5RWplkkkkmmWSSySMv9zX5vvbcAj5zkuD+G8s7+J1XqCjYji+NP0gOfQLdpWsDJHV79kd+9mSAYG/xwPjjNEUWKW0+11KYFhKRAAK2Lq1IwYnG95yDMDTerRROktWgBEKQdelOzKNQJs+zqhxMnKRB5PN53L78IQCgG7QhueBXuz8Cx0BCCwt5pkkmJmoYcvuBXqcHxRSLisI4EQpCJWtbKRdQKpD1qqPQIFrDwQjN3v2Ds/aKuMdxClHBPahKDdjs1nebTbzxo1cAACvXb2C7T17xbK2C53+SSvwre28gnthDacSoTrI37mY8/jaFxL7zne9A8r44cew4KrUaX1tAurSwZ548hmFARQI3t3wMe+SZvPfRW3jtrTcAAF/63BfwjW98AwBQr9cN5ZTL5dDjwHMpLVSYCrGsBLaemZnBFNeHeeudc3jvPLWoePaZZ3H6ND1DnU7LFEUMwyECb3xKSwudpJAI/jcAJRQiJHB2/HkQ+dD8TAhIRLyvczpVeDIKTCC8soQJTqcMr/g5CI1XJpSC1DHVrExdGqFDWAxX2hLwTYSphhbjB2Zfu7aKLmdZaAjYLnlxg2GEazeIpnziiRM4c5qQtFu3b2Nzizz8QS9vnt1atQ7JOuDEoRlMWORttldvwfPpnM7AgyXpXpTLBShNz7q308eFCzcBABs3b+LsCQqczkc7aK3Rb/mdrqG6Ds3MQjrj+40FN8ChCiFUW60yzr39NgBgc2MXC0coy2yyOMQXnqG6OrsXLfS5/taNq7fx2c8+RvP1BhiERHWt7mxg/jAh8SraxfUrXGOq3UOe0Utv6OH2Hfp8+sBBUzyzUq4jiGgfzs5Mo8oZiK3hENVCjCaFKLCn/Q8pMQIgpUSrRc/ud7/7vSS4N4ww4gyzteEIBw4Qyvz9730HR09QoPfJEyfhDUnnIowgAjrfD3ax2yXEy3Y7UEN6vsv1f/HAcZXLZaM3bds2CQpp9OZeQcvxefF302049iTt8Ofpml5RFBl6626EJ33t/er2PGzQ8nA4NL/V6/Uw8Glso0HXoOkqCiD5+XZtDZcR5ZybQ0xfSFsg4vY+iAIUcxy6IawklAQwSQ9R5EEynR76FgKP7tdg0IVScaHTHDM5gO+PwGoOQRDAsu///rjvCpw9XIVoU4+WWUvhpScpIv6HF1ZNFk+cxMwjQZKenEBWaZWXvjl3F0mKY3J06vM0t2lFCjJ9NZ6cJQTygh7aMhRkp3ffSaelUauamBlLSDOvmYOHceLZlwAAzUCjvURFj4aDPvMLwOKx0/jSj/9TAMD3vv278O1UPxDmJw9ONODxw1SrlXGYYejb15ewvkprKy1O5QMQRkA+FZ8TG2MawhSb2233cOXG8thzvJfsnxrOxo/htwU0pwm+/eqruPgBGXi9zg4O5+nhXl2+hce5SGNxYuIeqchJurrAPe2dv5Wsrq5i/iBRFZVaBWfOUPbL7s4uLt+hLIJjJ6ahFBmtjrUOpSnrrjYF/M2PKOvj3/+H3zbpzN/85jdRn6Dzt7ebhsH1/cAYhoV8HhZTizOzs/jBD18FAPzH//S7qJTIKPr6V76OPsei9Htd9DqkuPOWgjcaP7tna2s39ZgJU73bdhwDqUsh4LDytS3L0F6u45m0YkckZq20hKHqeiPPGHKFfBlhGL9stDF4gCQjhMo/0B73PN88u2EYwddJNp7vjZ96/9evXYRTpPFPzc2iWKT1D32NqSk6Pvfe+5iepLiN2Zkp9Ngo7bV2wVnXGHYj7KwTpfWFx09DOfTCC0dDfHD5HADgzXOX8PP/1T8DALzwmRcgFN33reVVfP+7PwQAXDh/GT/5ReondOz4PIYcAzPsd+HyIo7WNoHy+PF0URghl6e1OnvqON47RzFLb739DnbXiBp76fE5tGfJKJo/vIArHaJqPrp6Gb5HFO1Xv/ACbjRpPN1hB6fmydgWQRG9E/S8bu9uYMPjDNQgwhrT0cuTNUxV6fracdHisVlaolGmzydyLpxmEwCQc/MmG3U8SemV2IEUKkWbSojYSIdIsgyhIQQb19pOwsIs4Lvf+S4A4OaNm8jlab0jJfDSj1F81ovPnsX3/vTbAIA7ty7hwoW/AABMVbcRdemZs5whLJspZW+EOjufzuoQ/g6P7bEHzy6fzxu6O50qnu6rda/KxmnDxnXdPTRWkv2Z6GLHcfaUPkkDCvultEspjYEEYE8MT2yYjSNBEJrYw0qlAr9NDnboe+jxeo6GA6iQjRnlmxhJHSZ9xISljdGio9BQSioKzBpGqe4FNpLyN9KCcaq0ihId5tgmBERKGKctCAJTquZeklFamWSSSSaZZJLJIy/3RXjqhTxChoDbly/hueNU2+JWvY6NFvd6cRS8OPBRCwiGsiwoxNCJ1CnkB9rU+NChA4sheEdoCPYMc0LDZTPPlUCOO0Pnbcv0mXJyFsABaLZwUdCEMIyaW7hx6/rYC3B88QBsRmZ8z4fP0c/PfP6rUJos07ffehUyrr0TRtAOWco7rRYEB0w99eyP4Y33yMOfnZ/FOvewcRQwYupnMAogCSXGxMIiWn2ycPutFkRchE6FSWyqTLxxhaT+jxYChgMbSxTSPcISkftTSOnPJBDyvyfn5vD8j9EeePP116FD2gPK61BAGgCByVSyT8qrE6lgdcAExIkU8kN/S2oQjUtv/fRP/RQmuDBgo9HA5CR5u0888RRWNimDpl6popCn67W2t+Hxfa5OHcXKMlEGb9z+AB+cI0Tov/mX/xLThwg12trege/FXeuHpgP7cDTCgItNFopF3FwixC4INb725c8BAGYbNXz4/nsAgGKxgC4jPM1+C7nC+FkTy6tNsx6WTAIQLdsyQXtpL87NuaamVDqwUgppMgvDaIRbt5cAAP3+APOHiVKpViZgW/RsCUsm3c/3tG3RiKKklYYyGYfK7FOlNPwYzh5Dlu708NTTiwCAx46dxNJt8sY/unwNWtH+evL0YURcs2qy0YDs0bNVLxextU7ox+qdLi5coOdvsTiJxYM0l85uH5Uq0Yuzc9NQoHsXRUNEISNU0ZCCsAGcOPs4JjlTs7m9jrwbd5MXhiaJfAVPj0/beZ5AwNlY+XoOn3ueirUN212UuafV/NQkri9xscRjJzDT5V5aOx3MzDHKfm4JJx8j2uYLTx7Cxhbpqju3fcweJFrqay8t4o/+jAKeh34PrRbpyBtXCpjhQHohi3BYJ7lWDlPcBV5qZXSMFwR7MxrGFCVEEtgutEHSKQkiycLdW7aLn38VIC6Zs7y2gt//vd8DQFlCDqObERSKJZr3Fz9fxuHppwAAt5YFnnuKMvby6i9h27QHw5aC3qBjveUDW9ymo5dDV3Fxx5968Lxs2zZZnsPh0AQWF4tF5Dg7LB2oTPNMkjHS9Xbic6hP3acLBqZ7daUpsHStnrt7b6X1ZnxNx3EeCuHxvQHyjKS1dkP0OQSgN+hjfZvW9nB/aOrtaCSFg4PQN/opl08SVHw/SIKwpTStJZQWpnVTIYV6uTnX6FoVBpic4AQR2zFot23ZBoAe9Htw3Pu/F+9r8EgIWCV6AHSvj+EnbwEAnjrzWfzwA4JTpRUiJ2ghcxFM+qIjtIGPtLYhTIEzbfi3qivhyOR8hxcgsiuQDj20IhpBsuEhlI8hFxbq9bvoDeIskQl0Ao4zaYXoFebvO+k9YiExeHygOkXfPXL2BawvEX3jFovwOb0TuRzqU6QswsjHR5cpY+DHX/5pPDaiZoQ77TX4nAGwvboBqbgnUyWH9RWiourVSTz1HBkPVy5exNYypSsrFUGkYNFY6aRsRij9cP3CPh2asw+3fI/LKa1gcQruUy++gDPPUuG2er2G977zB4i/bNKkIZHUNk0bumKPwaPNOZ+O8bnvgPaRI4uLpsoxUlkKjz32GF59nQyh1Tu7OP04ZcTMzE6hM4ihZGDYSeD6ZW4+t7W1hWOPURxFqViCy/FZEso8hJQSGVORIT7/WaI/PvPc41g8Qi+jm7dvYWuLDKqFI0cw4tiSTm+I2kMYre22B5lq3GnbMeUUwrJIidi2Ddvm58mJUvC6gIzT56Vtiqzt7m7i/PsfASCFst0kY+zkycdR4MamkbRMk1uKCUji7IIgNhgiU24h8H1TUExrhTAc3xiQtmW+G3gjdLtEttjwcWKB4jMmpqfgsCFXqddx/kN6Rh1IDHvcyLA6hWKZ9JMFmPir4cjD9BzN65984wuY4GrDXq8Hjx0ORwJffuFxAECpOol+k+Jehj0fARs5gVIYeaToQ0TQrKzHESFck47raonnT1DG30z1MTgu7eF2t4XdHaKTut0m6hO0hze2B6jNkO55Y2kXwSWq9nx28Tm8cpPii4KwiNNVMvhnpxtotflZf/t9NCZJt436I1y9SjRZ4ORQqZFRN+wPTA+yWqUKJWLaNAc7N75xrlLEQZw9qwJhSiAIGQGc+g8BCM6qDbwRtrmInAp34HKPrz/79itYXaX7UCwWYdtxXGMXBUH6NC/P4dg8GbzzhRrUdZrH1q4Pb5f1e9dFFNF7ZQgLvTgLybHRuk0lB/75GPNzXdcYNkopo3scxzG6J10YMJ2iDuyln9LZWPF3705RjymqdExOmt5KZ2alrx9FUeIkWdZDpaUHwRC7u2TYtLttxPcrEgKSaX9p56DY2Jd2QqELISCsmEoN8MkVjv1ttdBjGvHu+KXYeeoNAvN5o5BPMvVUBCc2DqPQGGOO48Jn/TQcjKAfUCs7o7QyySSTTDLJJJNHXh4cts0WVu3oAi58mzz6haNH8eJxjv7v9FGwyOKztI8ce5IF10IuLs7l5k3/oeZWE3e4BHh7cwMDRnvKtQY6nLu/tNHH3PEzAIDesIcRw81Dz8OIqRMtJMBF+RQEInYldK4BwTD0OBL4ESTTZCEsHDr9HACgODOD0zNfAwBMH3sMW2tEVzR3djB7kLyyaiGPJfY8ltZW8NgZ8vD/5kfrqNfJyxq1PUQeoVLCKZjeTq1WDxGv28LjZ9EZEGrQWVtBkedrOTnEkco69BHpODo+MjDhOHLPQpFp+FPc+zxzvlIGFn362afgL5OXmBMaFpezH3tUBt9O6iztN/JxxHVd4yl1Oh24vAdPHD+Ol1+m7LHf/6PfRqVC61ptFE3dmA/fv4xb1wnVqZcqiBgpvHLlKl743I8BIIajEHu4joRij6vT7cDlwL5+t42Ds5Q1c3BqCh9eIq9m6fZtnOTy95Zlo8PZdVu7LUQP0UtrMBqYGjuu44LjyCFEkn3hOo4phDgcJi02RIoCFa6DGIGLlMKQKbnrH1+GfJrOP7zoYzTiIoqQcdcWqBSSo1Rk6Kooikx5+CDwAR0fh8ZzG0cWFhsIeW3vrK5jYoaeoUOzs5D8u6sbXbPmjltAhbsyr1xfQaVGKNDv//EPUbbZ635yAZHi9hDlAsIeI9N2gHyF+6ZtbMNnOqkgJA5yEHJv0ESPKTNLWKbsqdIaQz/u7gz0O+NnTA5HQ8SFv/yhD8XBu1eW7uD4yZMAgOefPYugT5TWpY+vYatJOu/w7AIK3LX6J770NG5cJdruz1+7iudOLvCYOwg4ALu5PcKzT1J/qcXZMooV2gNbWy1cuU0IUnvrOvIuIZmiUkGbixBt7vZgMYVXqZbvyra8vwgkge1xt/atO2vIMzIjnKSHW3/QhpNjFFD14QQ0p7zbxdJF2puvfPfPMclUm2VbRvdFnovBFn33o9dvo3WLW/hgBv2QaVhRQJ5766lpGwUuLCtyEcKQ5hoNLdw5P37xyFwuh3K5bI7jtjOFQiGpWRWG+7aZSOvce/XAis+jNUzQ/NFotKflRJxgEYbhnuytmDZKZ03fK4j6XjI51YDPa9iYnEaJ68HJFFIlpWWQrlwutwfFilEpz/Nw5QohkcPh0JxTLBb3DfDWWsPnbumVSsVcPwojDBlh9TwfQVDgc4R5f1u2MMHM95L7GzyRgh8XZDo0hxlFL7veD9/E53+OyM47m020RgzRujlM1enmV0uFJJXRkpCcAvr+hXfx5vvvAgBee+tt2Aydv/D5L2OKi4utrt80UGjgWpAF2uwDJSGK9FIRsgDFVFoYhijw5tUqQvQQqbCFXMIZwnKRmyKOf7u3i3qVfnfm6GnML1Lmz7DXhcsvv0qhiGKDINUbNy5jijf+V778MlZWyECaP9o2LwZhW7D4JeSPAow4el06Ll6Yoblv3bqFoMd5E4UaBNNhIhiYzRJoPJzBI5K8qLs53qTYY1JFdy/8ujebK36xTU7W8d/9m38FALj04UfQbNDqT2Vo7Z+PpY0CSP8pbeBojGvwDAYDY1C7rov1TYKnS5UynnjqCQDA0q0v4dXXqBJopWpjFNA4f/TX7yLHRtyhQzOmD9HVT66izzz9RL2KQZeUZuD5ZlRBEMDmh7a9tYndHbrnt50c1rc5U1Bp1PjF2h+MsMmNSu+sLKP5gBTKtLQ7TaMgisUiorh/jk54+lw+Z1LOo0ghn+d7kir2KR3HxNnlHQsznMJ/1fNMW63esA+Peznlczl4fpKREisyy5ImAysIQ9N3LIqUySb0fX9P4bQHieUUUJuk58/XNs6/R7F4UX+EF55YBADUpku4fY1o5Mpnn0ZBsOIrTeHGTTIS2q0e8nn63WG3DZ8bYrqWhiU55iAaIAh5zFKY49HIxwZnJ2lhQca91bSGxYal7/dMhomVk6YX2zhSqeXQ5hTr3dYWuhxXM1kUKHA2562rS8jV+GWZa2DbI6PrxOE83Drdr35zB4INv532EOt3aO+dfeIYPtmg61+6uYYnjpNztnCogSYXwwyjAF95iRzKize3ceUWOS6FQgl1joUb9PuQNtMGVh72mD3ROp119JgWGw1G2Nmm/Z5HC3Ffx3c/2MT59yk7LVIjzB5o8O8Dh2c4nRlDfPghzckPYJr3er5n9JMjJNwSzc8tn8T0cXrOrHwFuYjuoReFGEa0HoEcInA4S1IIeC0a5/b1Ljqbu2PNDyDqKh4PkPTNS1NXtm0bhyNNLe3JOk4ZQulihoPBwOyvdGyP67rJOyAI9lR2T2c179fU9+6YogfJ4cWjKBVpPVutFra3d811Yt2jtcKQywL0+/1PNVWNx5keW6wPut2uOcdxnD3vmNh4S8dHRSoyFcRzuRzm5ig8YXt7G6ViHMOoTXjKvSSjtDLJJJNMMskkk0de7o/wqAh31gjuv/3JR9gdkXX8/p99G58vkEX21Ne/iAqXQa9qwOIAIisMYbGXaEmBzoDgyY/PvYuPL1C341x/CwWPg/M+egX/9Vf+DQBgZ+4l/PYf/DYAYK29jvmjFH0/Pf8YBNNYgTeCUyBYMbAiBFwkUAkX/kPA6F4IhOzdoTiNAqM03X7f1Chx8jnkua+MA4kit1TYarcQcBDns8+8AMEo0+z8YSyepuDeKAL6HMDcHQzQ7pIXNxgMEcSZX34An9GeuSefwZCDLEedJqI2eUjh6hA2122JwhD6IYJB0yLk/plZIkVp3VWacM85sSHu2hZmJxlSPXEINyJuOfdK/QAAIABJREFU+aG1OWlP8clPFTwU5vw0WJUEumPswOzV1VUDgzYaDTO/Cxc+xBSXxP/iF7+E+QVC0T68cB5NLkn/Uz/xz7CwQMXldBThr7mWTru5g61VOkdKiQoXi4vyoYFZAW08nJXlm2huEFVbLZdRLBCtSoHEHGSph0n/LAG0B+NTIc3mljnO5/MGyZGpApxE7XH7DmEjLNGahGFoGEQthaltYSFCa5eevzAK0OEg4bW1VWim9iqlMkaGugpNsTAhhPFCg8A3wcnkddKx7/sIHwLhub26i6U7LXP9lTWa8+LBKTx9huie1998F/4WITl6ZwdXP6LaMpOzh3HnOqGtuj/CgQmmLiIfDKKg4HgI44zPmQNY9Uj9rTfbqNZoL9csga02jaFQrMDneYkoRI7X0PM844WG2sN0Y2rsOTZqdYz6BM13eyN4Ad3Hoa+xyb263NqE6dherBXw+ZeJWi3lAwQcAtDsdDDLbW1OHzuBlVukp899dAunz1DAfL00D5+9604ksd6KvxuiaHEW2KFZrG6R937h3Js4dYYK0UxNT8NhZMz3htBjdktfvfoB8hzg70oLByZoD9YqGh9dovvzw1dewZ07hIrV6hW0Oi0+rmLpDndoH3TgKw6arhfQ5Uw1ynKie1ifKKM4Q7q450YILLpmf7SK5hrd9E+uLMFjvVyfLJr6PxPFGhyf++y98wkK9+g4vp84jrNHh8b6IJ/PG9QiXYssCII9qMt+CIzWSS++YrFoAqE9zzPPmZTS6LnRaGT0YxiGe+iw/WrYxWs3rvzxn/yFyUz1/QhFZmJWV1dw+nHaX91eb09vr7uLCsdjjpEuz/P2bYeRy+XMmD3PM9dUKmmlYVs2+qw7G40GzpwhhPKVV34Ez2dqMgqhcX99c99dfP69d3H+KqU1dtqb6HRJAV348C0c6NFDcnrUgXVwEQCwOfKg47bw3Y6peuhC4ALzeKsfXkCBAxD6no+DB+hl89P/+GVMcDWvSA6hd5cAALs33kf/Dn13ZfIwKjMEZU02jmLAN2RjexUi5OJP5RK6Iy5z/Atfue/kAWCn2TSR5tVcAwHDyrmwDI/HGUFBWXQshUBnRA+f3+8gX2Zoc1SEwzE5zkjBi+icnBSmsemEAKYnSUlFE1VsdciA2e0P4RfIYPN1GeUqF1ybOoDAp7UdNg5g9wN6GRfcCAe4+Nq4Yoo54R6GRJrm+lQfq/htKUw2/KC7g3PvEd8+GI4wbHCT03wNklNLLSlNNpbS0Z7GsSZT7K60dDMG6LENnl6vjQ6ne0dRZOitnZ1d3OLyADdv3sTCEdpr33j5ZVS4AGRjsm4U9PVPruDGdaro2+/18PFFgt0PH55PZVAoVCoxf2+jw0W4mturGHEMWj4fUlMvAEePnjRc+9KFSyi4sWGoMArGNwZ2dreTmIBuEreTz+VMyYHhSMJmPjsYhUaBSilNsbZ8wUVcDNuCwvY6vSgDf2QaunY6u9AcIzTo9xCpRAHFIgSM8g2C5GXh+x6CMO5R9HAxPBcu3TTxV7O1Ciw2DA7XclAtojfKYQ8nFyhTqep7KHJMn9rdQYXn+xPPn8bTR4iOzokQLU6p1QULiu+Frcp45W2izG6t7WJ2ivbDz37lcXhxpdrBEEMuHZGTgJacYn8X/B48RKHT0UghlyNaqjE7jQH31ZrI2xAhZcVEaoBoxJSAGpm4xWdeeBLLd8hIXruxhWKF9FyvNsLcUdrbr776I9S4H9xzZ4/iOlM1r53/GAUuhindinFuKjkLX3rheQDAH37/Tbz3JoUbnD7zOCanKSbKcmzYHLPyINH+Bto9ug+u68ILaby31wJ8789Zj683EXKBwVHkYdim+fk6xIh1ulbK6JooiuCZZytvjE0gxBKHDrT8G9Cc7dXvaOxskv7t9Qbm+ev0uqZgp264KPC+ditFROH45RMqlcqeF3p8/XSRQMo8Spp7prOl0notHbsSp7rfvHkTS0tLAKhx8THu0Xfq1CmTcZg2qO6mg+LfTRvmaWNpHCmVprC6ShXHbSsPwTFuGxsbKLPzpyK1t49YbHSlPGMhpFHvge/DixvbSrGHnku/b5L7m55XYHTeTrOJH/zVD3juEaATA0/K+xt1GaWVSSaZZJJJJpk88nJfhOf//n9+By7X9ShXSqjUKEDsqaeew9QkeVArN5ZQ9rgOSKWGuByAU7BgcTCXVa5jigPsTpVcHOc+LreXV5HjYOYTTz+DgM//zu//IW6vcCv4/BQkW5HD4S66twn+7CzfQp+t/ma3DcGZD0JKjLzxrfVnHzuCkGupRLka1q6TF3K0XILMEVKAMIJBJKSk/kKg7LPYQh/0u7Ad8qJL+Txg0TmWlJB8fnNz03SCnW40MD9Na7hVGWClT57QSAAW26F+qDHwyJoe5QsQQw6ObK+j6oxfRCoJWQbuFQyskAS2CkjusM5dsbm2gYYEOOPl5o0buPkheYPt3gh+7hP6rsihVCa0Z+HIPOa4i3pjchIOlwYPI4WkbrwmKz0eR1x3CHrcmGVUyiU0dwhpuXz5MmociLuysmLg4Gazidu3Ce05ePAgJrhtRGOiYSitWq2Kr331ywCAP/jDP8Inn9CcpqYmzX12XddkQk1PTqBeoXuuo1NobhBa4vkjVKeL8fSwy0GtnU4XpULcpVrgIVB09Ptdk6Vl2w6cGHFS0Z76Gj2uD7N6e8VQsrZtG4SnVi2ZAnoWIuw2CVWwLQHFdMlwNKTIeJCnHcXe7F3IX+xJ+r6f6v3jIVJJ7590mfsHiedp5Jkqb7gO5o8TdH620cC8Tet28JkXUGK1VXOBA5/nJIOOh6fm6Hh+ug5/yIHBgyH6jPgKr20CqpdureLNdwi9Vm4NS7eIGvvM41NwmE7otvvwGeWtNepQTMtHShmvfvrAITwMuxyJHB47S3D82Rc/H3fHwa0P3kCXKbxwtIPtZbpovnoAsk5r0h50oThLq1awUOYu56vtLtqMTB44fAS3OcDUuvghZnlNjs4WAA7IX9sZYmmV6CUv6GPxGNFYp48ewl++QUUyP754CQuLtG4HDh5EUlvr/vL9N98w97xSqiDPz3O7PcSHH1EmlB8qo3N7/QEcm3TZ9nYHPtMTewJghTQoI5AgGiNviNUVLh6ogBhgGHQBb0hjKJfycONidDJBI9ea65Bc2VBXHRSnjow1PwBGdwCf7kK+zUX5SqUS6vX6nvGa+fDclpeXTY2ua9eu4W3uq3b9+nX0+X1g2za++tWvAgC+9rWvYYd1SbfbNecAe5/FeP193zf6L13wcByZnmwgZESo2dzBzg7R+xoKNU7mmZ09CDDqq4Iw6XMJbbK5lA5MrTJEkem3BSlN6EkYapP1FgS+QZoPHJiFiuucBT5sTlLSWmHIxXxLxSJyDFnrKIIQf4csrShfRYX1tm0BskSDeuGf/wyOLlK64xuvvY7OLsFsJbgoVshIqNZrqHBfllppEr5Dx/52FyE/PMXJaZRdNn42d/AB017/+Z3LEKUT9LuFHMK4J5etEHHWku8L6DJHeVcPg5NuECplUljHkUoph7imYHW6gQOnKFZguz9Anl+cUlimnJFjAUJzTIYAIONCbBHAsF9rqwnFtIecnDINF3OFAkJOc91YW8YyN488ffIxlGdJMTUDH1cvkSJeWV2Bx3B5uzfANCu4I4cWEbRXxp4jyf70UFLgSid9SIRIpaFGgI5hWgmfM0ZWlzdxu8kF3TyNIGia3+kwJ//+u2/j8GFSJPPzR3DkBKVnH1qYhx0X7tLpFPuExlIqune2+l0yGA4Njz4ajdDmGIwwDNFut815MS9u2zaW7yzz+QFOnaS9fOax4wh47/T7fQy4SFZrd9dkYtQnaqafm9BAtUT7d2ayjimu8NzcacLluB1IidGQlM7Ozg52OFOm1xvAth6it03oQcWpi0KbTCshFIIw4fjXV6hMwvbmponzCaREwC+SUW8HMi4WpiO4rCzytXqSZm5Zycsgikz2XlrS2VdBEBgYOgh8QxFHYWgyvMaRz33hCeyy0VirFFCrEG3r2Hm0WjTfWq2MImePONJDnQNrGvUcinG6ejgCuCrryHIw4Bi9W5cvGOO9knfR5lig5eYSTizS85fP5VHi4n6IthFJ2gMz9Qq2B3TvooGP0gQ934+dPmUM7HHk1FMvobVD19naaeLkaSpyePjZL2L3AD3fl869itWA1mHzxh0cnqN9dbS9iGGfdMaBqSJyk7R/otwMNPdr21jfwEE2ct4+/z6eaNO6TU2VcLtNhlCjXMEnS3RfGlOTJn7p7MkZtHv0LLxy7iJ2uX9Sc3sH9cnxSn2sb6ybF7rv+/A5Lb0/CI3hGYSRoR7CMERsy2idNJEUIokJSfefiqLIXF8Kid4un68Aj+MgA18aKrVQ1MgXOLTCteF5/G6QEl2mh7Tl4tD84bHmB9B+v3GDjLennnrKxPetrKwYo6XVauErX/kKAGBxcdF817IsYxR9+9vfxquvUpjC9vb2nhiemH5qt9vGcKpUKnvic5KMySTbK51J6fu+iTEMgsBQZuOIEBpnz1JmcrPZxCuv0H60bdtUJY+iJEtL6HTKukhRdYlvK4VAjt/3wrHgpN7TcQySEDk4TpL1Nhp55pqxfnIcB1ZsEGttMoeViszY7iUZpZVJJplkkkkmmTzycl+Ep3boBOYaDp8YIeQaFoufeQndiDzqH+xGuLVKMJuz3E48T6mQYwi+KiSsGO4KIhQZDi6UbbS5wNnSVh+rbTretSrQDnlQllNAEHfcRYSII+6V0tAcoKm0hGJ0RcikrPU4srrbNp3KJw66cOJ+IIMeiuwl5Et5WFZMG2iD2FgITEx4oDRuXqA+TNfeeQuT7CW+8JWvYuYABVpHUqAySUHOeUtgZZlQhuurqyjW6fNAAJ0ueVaVygSKDPfWZ6dQZSixvXMVtp8gF+PJfplZ6SJSAnGFuShSJkBV6QAqrjwnJDo75CUOWjtwcrT+1WoFT5+knkC1ah4fX6J2Bf1uG/PzlBlVqU7g8nnqSv7JhXOY4O7mpx5/AsUyefIaCXJh3SObbD+xbdtAt9VqyRTNW1w8apCZjY0NQ0N0u13jBbXbHXT7dK+2Wh1cvkqBrIVSCTMHuIVIEMZAHoLRAD4HsDsowJaENkhEKHMp/F5/hAJnELZ7A5z/4AIA4ObSDYM49ftDFPLFseYHcAaCjlEdCgAEANu39hTwGjIqFQZB0j8LwgSS6yg01JVlCeRMB2LHeMWR55n2B5a0TJ8sGgONR2ttUJ10758wTK5PrSXGp7R+5d/+W9y8RRmcze019LhOihcC53cJ+hfrK5ibJI+3aNuocVG5kpNDnjPU8lIiH9N8RQEw8iPsEgbsJR46u4jTZwjNuP3KO5iYpmtWJ6YgA9onoyEwCggxyztWUuxRWRD8W30ZIF8aL4MJAEr1KWztEsJz49olWKwYZ2YOoTBBemLx8ZdQmKDfXfnh6/AYvvaUZVAS7Vu49iE9Z271IM6cICS15kQIuG18sVTGpU8IiXiudBQVHmahaCHnEhL/9ruX4XK9ppNH5jDPVKwjBG4skX4a9AaYO3RgrPl99pljhgas1moYMYq9st7Gh5cJUYsQQnLbCikUfJ/uc6QiWCJp05GmtZKWCklG0kS9hsinz9vNkUFFtLZhMUow6Gp0O3TPbTupCQNhI+QwiHKtgk5nfFZgeXkZv/VbvwUAeP75503hwe3tbfNM3LlzB6+//joA4Jvf/CbWOSt0e3sbN29SMPCVK1eMPkgH7nqeZ4KTFxcXzfFv/MZvGHRoe3sbLUaLO52OOSed1TUcDs140rTar/3ar401z3TLjBjFEkKb0IR0XZ20CJHiB3RS9DTSAiG/V3LSMT39fN+HxzqbahnR+bdv3UGpxMyQ58MSiZ6L90A6CFzgwfXp7vukvn/lFjbKcVqYMFU+d0bn8fEyLfxycwTt1OOpGv4eKkJMUG9avumT5VgW2vwQzObyqDBNtrG1DXBsxIsvPYWrvCkCv4Mq0wO2dNDlXwpylkmVFEIixkXDhwmMADBSAiNJxltXS4Qxvhp6mOR4CzfvQLLBE2qBQMWZBLYxflS/jfVPqCDa0yePot2jF89H597Gi58nDtZ2XOxwReVaLo+D81Qd1VcKHS7i1g08zHFlXhUqdHv0QPj9Llpb9ND42kJoJXEbDyfabCggMXpsKQzep23LVEJWsAyVYkkLrSZDqspDmbnxWlEg7JJy7PRDzNfp8/zUFIp52oBTswW0tmnMf/7aa/AtUrKBH+KFlyjtFpadFD8ExjZ4Njc3zcu9UCjA5QdSqcgYXFNTU9jd3TXnx5lcMzPTqNVIYQ0GAzicsXXy5EnMzpLB0+10DIRqORqI4orHFgaDOMXUMtRGdxCiWOX+R7eTOKKFhQV0OhVeeIlOe3yIOQwixHEUtEXZILU1ZJwqLiVsN86akHt6wJp0cmiTuq5tByHHxthOER7H7UgVJD2zpEYU8+hhmGpSCPhxtWGVxLiFYWQMsygKETxEgMuoW8XLX/15AEBneBUbO1SteugPMOywM7TZxKjPfZJ6PkZ5rjwb+Ai5DEbBtpHXMY2RR5lLWfRzEbaZUi56fZQ4s/DgwhR8biS6tNHEgYk6Xz9EJUfHO4MAPbZ6ndkG/Crtn3ZBoseNi58aY46R8lGr0R7odVsmTmxtfQ2TfM1hex0zHBv2zJmT0IqMvc3mGpqr9FtBq5fseUej4NLYbl/7GI7Dle+FxM0N2vOTK3k8c4oNe2+EQ4cpHvPipWu4doPo6BdfeB4nTtI6n7q8ilvcX2p3t4kwGI+afOyJWXPPLWkBkuZanSrjL/6K9GNzN4JWcTyfh8WjRNlNTlVx+SIZer3eELGtbFmWyfgtFlx4nMll28CxE3R/omhoMk2VAtpc/TqKNCyLCxLaEpLLEjjCRovj/nKOC5UytB4kly5dwuYmGW/f+ta3jBE1OTlpHCzf943B8Kd/+qe4fJmKO66urpqYn3K5bI7n5uaMviuVSgmFXq/j4kVatxs3bpiyFoPBwDht6cKG6VidtDEipdwTa/Qg8f/f9r60Sa4ju+5k5ltr767e0Y1uACQILsNtOJzhjDSLLdthSbbDskO/zWF/1RcrHFaEIyRLoRgrZhwxQw1nKO4ASGJpAN0Auru6qmt7a2b6Q96X7zVFAgXri4N+N4IRhWJVdb1Xudy8555z0tT2ACZJgjwrIOsMDVo7GWO29yYMQ5uEaF0aXUO4kAQjM+GC60Js0K8kYzmK9SPLSg+yTkdAcOrbwXlV6q/rJTSQaA1p1VFHHXXUUUcd/5/HE1O+uw8eYb/Q1GAuGuSNxT/ahyz0DHwfCTXSJblEQHYSzWYAt0i2ZAKPsr/A8dBdM6ffi2ttvPaCaYza3t7Drz74gF6jsRea5uEgj7BL/icT5uO//4PJlI+SiDxZAI85QF6I+Bnn6oVvwN5LWG+bU8LG7nPYpYrAo/d/B8zMCWCpE6BJJy7FhGWHTVKNhMrH83iKKxeoJH1xB5/eMI3H8fgMh/t3AQCbF3fsCfkk19BUhpRKWYGwaRLjLmkfDe/fRX/VnMpC18F0bl7fEA0MnkHDZZHQWoHRCZZDW2FADliChtYahf/AKFfIqVrFshQsNaeBdujBoex7KnMoaU6JH974DPeH5tSVaQ5Br7n+0YfmJAjg4qUrCOnE4Pnewk3LX375BUJiIW2sr6G3ZE6M82iCmLyijFeNGS/tdhsOnY43Ny9giYTj8iyxp5d26MOj10xVCeUMTo7ASNSuEYYQaSEBL+HS/BC+jwHBMbmUWKVK0cH+PqakveS7DrygEDB8emRZfq4xsZSqL0v/jHP4VKFqVRocsyyzsFQYBrYkrZRCXkCdQpQnc1VWAZM8sY2kWuvK6bGEPc0Js3R9lvL/jqX1yed/jbv3TPPo5cuX0Fs1MGngxxAwc9ENVyDpsc5iy/LUUoHTOA09Dwlp1/hhgCXHnPBxNsaUTv6Ht25Bka/WlY0OmoH5/tf/4X2MNw18k88irJHeFVc5di6Y7zZNY0zJcZ67HsbpfOFrvLh7AY9Jb2w+m9jG+AcPDvGQGJBtJ0O/bcaSL84wODPz/rOTCCcHBsYIeITlVbOOqkmCycxUE1Y3muDavPfO/glS0ie78eURNntmbMd5jk7XzLO19Q387iPD8ArbAZ5/zqx/G1vruPq8qTQ/fHyMiDy2nhbMg4WlNdO22geWQRT6XBpgBUyea/zg+wZa/ME713Bw31SbDh8MMDwt7II4etSgvdxt4q//xuwTp8MEr79pKlWOo5HTtbq+REoVqTjOLekiyTMIQgsarocp+do9PFrCyXBxSOsv/uIv7OM333zTVk6qAnrVCo/jOHj1VVP/e+eddywE1uv17DyuVoem0yn2yW9yOBzaMSJECV8HQWCJGlWBwar3VhXtcF33mdzSoyjCgCxW8jzH1ReM35ofuLZ6k6ZZ6e/neXDdEnHhdE8SyZAWDGrHhSMKOIzb6k2z4dgKtPnO1IgOAV20WeQKjnteqLG83uK9Gt4/BdJq93q2hJZKgUEBYmuF0DNfqscirC+Zj9levwifEp7RZIicFIanJzO7CbZ7HVwmk8WLyx2c3bsLADjdv4fRiSndXl3bxEVadHym0HXNez8aDJEVPR6Bb3t7pkkCh4pVmjsW814k/u2//GP0yBitHYQ4uGcG2umj+7jzuem9CJstrFDi8fy1F7GzZxaCpW6IuDAtPfPh0MbGGcclErm7e2cfxwdkiNdu2E3FFx48r+gtcDErekomU4xPzEC7+/GHiKjXxQ98bJC5IG8tI50u7v0CVLXSWIXhw1BAI5WHMAXEgoosLJUXWiH0qcueCaTEfpk7HjaXV+md2ipiCuFCFEmpq9H1zO/e91fgN82k9z0fB48NPDrPgI0ts4Ctb6wvPEF3Lu5AEVNJZgmGI/N54I4txbaaTUiaeI1GAxt0X3vLa3YRVDJDs1n25BSLS6/XwdnIbI4PDw4h08LI0AM4STIIjrgw9vN9TKkP69HRI7j0+d12B48f0q2UCvNocaXlJEnswvrV3oZqwqPoepkjwGmxCFzHwlueJyCImZVlOZgo3sugC4FBlItmtSxeTXjyLLcLX56X/QfG46fs4XmWhOel17rY/9L8dje+OEXjnmEGba6+AL9BpXPRRnfFHCweP7oBLWN7XZIOZ7N0UvhzIp/naFGvWcMJsLdifveU+/BeML/LeDZHRjB7s+VBRiYpXW010GDm+zc9F25oxmwyP4JXSCmczgG+OBMtTmZWGbbZbCGltSoIW0hpPMzmI4QJwcXNZTx+ZL6bUhKvvmHYq91WiMHQbIR7lzdtQrJ58RXbi3c6SfCDFZNMDM5m+OSuef1Wi2GLVNI3L2zgk7tm3f2rv/019r4wCU+j04LfMBtqGPro9RZjoqmvjBer3q45CnYy1xr9VfN7zmcaK33zeGlZoN8368irr21CEXScV9TwdZ7igw/MgWMwGILzwmtpjmaT1M09oMGK/jgBQUlxKjNEdFh1tEJ/1YyjKBPI9eJ+aFlWrhNvvfWWhbKrkFEURedg9qJ/sNlsnlNmLuZZFEX28WQyscnPcDi00FLVZNNxnHMstiKq4oKO49j56nleRSH+6TGdji0l3HVdbG6Y/Xilv4QG9R6engywsWHW6+FgZNcYs2YULC2GYki4TrNUgufCHig5BzLCL/MsRZYWwpWBhS+1ZvY1SGBlCqQqPcgYF5D5k8UVa0irjjrqqKOOOur41scTKzxRLhEVjqw6A6dTv2YcVHRBI5niAuntvLK9hhM6Cd++fwcdOhWst1vW/+aF9WUER4bT/9tfv4vjwoXVcwCqeGx/9xpi15zEBk4bn56Yk8m7N7/AlASHHD+0nPs8y6HoNG5KXIufKt+6fBGFEbDrBGgR86vp+bZh+P6De/jspoGZPv3ofaysm6z29ddfw4svfcdcY6eLe0emMjM8HWBj3ZxUrifXMSfPpLVoD0Pqyh/cu42ISqqd9Q0s75gG5oQxNIjJ5bV7OCJhuM1Ll7C1Zyo8zaaH/nJz4WusnrTOPY8qd4vh63qEmS6ZU1xwI6oIoOV6mBGcl8gAK2t7AIAsirB/aqpkYRjiO+R5cunKZfz6fcMqmd86gN8217i9vY2w0OTJcgSheZym6cKQ1tLSkhW3SpPYsvcYF3Bb5hTheT5iagxvNtr2hAZo69OjZQqPvstSu4kmVf6OZ2PbeDyfz3H/fgHNCTgFzMuBg0fmpHxh7ypWtsz15VCgSj6Wr1zBGsG5v3r3XdykxvxFIs9ze6pxhPO1DBalFCKyY3AZt43ZYRja35bxEmZwGbMO4Fme2HsohLCVHc5EpdqTW88sKXMLHVdP9VIqW+GRUj61ibAaWZrj8guhfe/hA7NOfH53iEeHZt5MxhF+9rPfBwC8cPUdTKfGWmAyf4yIKosayo4pmWlEhZy9YmgTI7C53MdeaMbG8eAYQ6qqbuzsYka23r4G1Ng833QdDEiXZjKZAlQ1Or1zCB0+QzNoLtFcMmPA95fhhmbsnY1HtiIwVRoPiI2zu7mC/opZz1ZXO+DUXK1yswYCQLfRwEbPnLrvHkys8OrLr14FZwn9XRe37pq1eXT8EMefmIZwt7eD3R2j2/PxZ1/iETHItpsd+A2CXvop3nxzkZbsQviu/P0LmCOOchTFPiaAvSsGRt6/PUdIc17mCWZx6dvmuoXuFLdecGHoo0ctDoyPsLRE1VY3sI3QSZpgRg3sSZKBM/N8u9uywq+e40BK8/mj8QzLq4szJrXWtpJS9YqqVniMzUGpR1SFYArtGiGEFQ88OjrCyoq5J6PRyOr5PHjwwL63+jnfROioVny/Kor4dYyqbwrXZfjsM4NwrK+v4eMP3jff7fTU6go1m020qPK6tXPRQt95liMhAdS2cm2FR+awEBUTjmVTR/EYUhWkCmH341Zl925HAAAfsklEQVSzC5fEfDWY9fTLtUREY9xLEzCCyZphAM6e3M7yxJk6m87tQulwgR5hwAAgqSw7nOXIYrMo3H74dxgQE2aWpvg+wUBvbLXQaJgPuqBzPPzcbIj7t+/iiC50fXMDPaK+XR8d4b2hYSSNZQDhkfKplHDdwnY+BWcFewjWw0tJaRlbi4TnNSCIIukJD7s7pix6afeyTRIymeCYlCY/vf4p3n3PDMa//Z9/hV/+/O8AAC+9+hquXTWKpVOpkBciUmGI4/tmURaQWFo2sE6L7eI2de4/vncPSwSH+e028lUz8F/86R+gTcJ2/fUVLIVmk15vcoQb/YWvsSroZ0An6prX7JzJVkET1IDFwFTF9lMphZBw6ZX+EsbHZqDNUobPb5n7k0QzKDI+XF7fQNAxiZ/iHlbXTVn0ig4gqcy8sbaCLmHa0OVmrLU6t6k/KWSSQdDi2OouGaVgAKPREEVKFwZNdDomAZcyxYz6s9RkZAUy/SDAybHZaDwubF9Qt9PD+Mwk7OPpCJOx+fzDUQZF2HM7dO17O8ub6K+Zaw1cDz5BWr4fYJnECb/39ts4Pis4h0+PPCsZUspRYHRvfM/7Wq8rMG4Zb0mWWqE34VT8e6Dte/Mksp9ZpXpy7qJAb6SSlb4dZWGGKkvEJDzFwpd948L8deH7LqSifgUeYGXDjLVweQxB5LZbn0X48//25wCAq89dwz/7qVHGXl7awvCO6e2AOIPm5jcK/DaOjkyScHjnCN99/hUAwOrqJgTRyU+zBD3f/C2xfBGXNszho9cKcXTjQwDAvU/fx4MjM49zrcGocj4ZnkE0FocKmICVI/BbIVo09mdfzuDyIknL0OuZ5xkyOPT5vaUeUmKonc1mmFIPUho14BOEE52dgtOXW+ttW3hLOD4aDTNHHj4Eju+bcXs6m9i1PAjatg9Gqgx7lw0s3+s+j/7SYgcsx3EsdCxljoTm4nSaI88Ks0gXrU7R5xPDodNzksU2aRHCQ0qbmtIZGKPEOcsRUoIpZYqI+gi391oWApvOcviU7OeZQpaa8ei6Aswp+uByjOdkeJsL9LoF0/jp8U3KycD5vpkiKZJSnlNFrhp9Fq+fTqeWrv7ee+/h1i0jj9Futy0TSkp5roen+neK56ttAEqVXleL+hIWcXZ2ho8//ggAMBxesD16g9Ep2CGlDQwWLgyDJkJiNR8cHNhEq93uW0FC4fCyHxCllMhkmqKw45O5hMopKfU5kqQ4ZEzsIVXrqnFxBl78pnmMhv/kxLWGtOqoo4466qijjm99PLHC4wlum6ccx0GDyriCK0jSekhEDzkdATVjCEnwiScpzqj56IODWxCkOZKPRjgemNPyPZkCJICFrg/VMPnX/XmMMZUbU63gRYXDalkByGUOTdL8jJWNuMaXafEyeiFpCBibgyLTZOQoBQCcO9hcNaedjdUtfO+NtwEAX9y+id/8znjPfHbjJnqUiV+59Jw9OWupMCftjyZT6KyYk4S3tYHLxFCbTGcAleAzwTAnZlEWNpCQG3eYJ1gjqf2QAx138TK61rAVm9Ixi667OMmjvGsMZeWnmhEbfQXSSGg1sEOwnWpuo033ZzyfwqUx099Yh/TNPTmdxugQe+o7axehbN2o4uHFGLgVmeRfC8N9XcwmU3CfGuBc17IgAGW1Ynw/wGxm2C6TydTCPZwxTMgKY3Nrx2J8DMz6T62uLuNsZiqXt/YPMZua8TiL7+ECnXw3LiyhvW6ub211HTPSYcrTDNGAxMUaAcYTcyrf3ljDT37vhwtdHwAkaVo2Bary7lXFtrQum40dDuSFHk6WwaFG5VyVrmmcc/CiapQrMF7oXFR0mpBbxl41sjxFkhTNtKrStJyXjYaq1PxZLBRcUZ7MFVVJPQgQIQkrP7yA6SumCru/f4g/+69/BgC4sHYNG+vmRdzTaPQL6KKD0YSak71LuJ+Y92419hB0SSjyYQaH7Ac2XvwhNnf3AADzwSGOxkb6fzAeYHPTjPc8zTEkFl4sBCaTxZvPo2iO1Q5p04QtnA7M52xsbiIoWC4XtjAme5Tp2QAxrYX3HjzGRtfcH8E4NqiRtNHuYv+egVM/un4Hyz1asz2NiH4j1/PsmBSeh1e/Y5qZT2cJ9k/pWqIUfbK42d3bhKJqW6vVhe8ttt5Ux4KSypJeJuMMGY2LZtPH1gWzDt697Vr2jRAaqqjUK1Y6X2vPEiqkyrC5SYKg8iPLtIoThSwp9wCP9irXdRAQdClzBYe2PM04RnOyqHAEWp3Fz/7VCk1V3G80Gp1zSC8qPMPh0EK7rVbLvj6OY2v3cOPGDWtXMZvNbAWnahUhhDjnrm411Bzn3OMqS6tafXoWltbx8TG2tkxbSRCEVvCy0W1DEhTlex4UtZUIwa34oed5cEgwV6kchS+T53A0gkJwkkMrM5Z7XWH3S6PDY17fbretGOrKSh9al20rBcPLwJ1U8csllHzynvHEUeyI3MJDKufIiDnguAw8MwPZdxwwoqgrmSIkjLQZNhDQ33ZlA6rAy7XAQ4IfRDRHh1Q+l7dW0SKPnNE4hp4QDpwDU2JNROkUEXHdc82gqJwmuIOcJlaepc8ksKRkBl2US7WGsAwmbsEcpZn1FgJjaBMb4HuvvYNXX3kDAHDrzhe4/8CUvNudNsbUFxLFESLq4fng3XexTYupGwawYCW4paWfjce4/YXxFJsMjyBg7sOrb7yEa3t/CADw3BDKWbyMfo4tAUAxwigY7PMKDExXJn2hqMtgx4CUOWLCn13BsblCvQibO+hfNlBBphRyO+kVrJusALKC8RdFdjFzHGFd/yqAmoG3Fr1AruFUsHmPku6NzU2MidKrNaznyvb2NrbJO6fTaeOYoKjDg0foUmn7bHyGbs+Mx0YrxJWrphfpu99/B3/5l38DADg5PsHllvmWnljC5dfMWAiC0PqJ7e1cAK3heHx0hPHYJE6hX86hRcJLSi8jVum94XDLpFTDLhZC+XCIwZKzKXLyoBOc2wW3ulBKKW3GW000Gc8hCeYQsgGhySMuk+ApjUFV0tKRpuBZ5Zd7llJ6xXdHa2XfK+AiJYV1KRM0mub7fO8Hl3G7b37fj967juHIrAetToKVmJKftI+VvmFV/vG/+hdWDqGz1MCA+g118wSSSuQyiXCfYKxP3/0l4gOzCe2sb6DTM9ceTyN7ryRjiOTiByzfa6DIqkfDkYV/Ll68iDPyusrTFKtFMtNqw6FDw+EXH+OIFHu/uHsfk9R8zuOHW5AENTb7a3jxZZPMeELjwZeGIbqysgo3IBiAKZzOKPnoLWHnoulJfHSa4e13TE9is+0ijkzC0wh8e/h7WqRJUm5GAHzqz8lSIKZNc33Tx6VLJrG6+WnTJn3rWz1EdLhVipW0a8UrUFEC5hCzEAK5LCA4hVliPsfhIRSNd8fxoGntbjWbCLxCyV3jwamZH42Oi6XW4nsG59wmMKenp7b3ZjAYWEHCJEns3JpOpzZpYYxZdeXT09Nzj4vkpNPpWBjLdd1zyUxxUKsqnVfZW9UeHtO7V0pZVBO1p8Xm5qZVV/Z9Hw9p3DHBbQtAv9/HhLwe7+/ftwnVSy+9ZNfUyXiOgH5HxqRdg1vtoJTWUD5m1HsYzadWAftsNLbrXLvdsQcvrbU9wDmuB1DCk2eJZT5/U9SQVh111FFHHXXU8a2PJ6ZDjVBD0EmcC2ZPETqP0HVNpjaPZ+BUbQhaAoyyMxcaDa8QXlAgGy54GvBJJFAFPnxqyuUOh+OUGgOFCF6Up8hJC2MuY+td5fhBWYKHhu8WjbgO9LMwQ7IMQJEpZxCi0Dph9tTKGQdDqVfCsgqgQI2+Vy4+h+WeyfQ/u3kDt2+bprPHhweYk47Cr37xC4D9EgAgPNeeZpVS5yAk0oXD2oVVXH3ZNEK/9uYbaPqm6TZlLu4TXLgId8Jk0harsc3JjJXfn30VQrIvV0ioOXU0nGBCJdg8jdGi9/q+A59KyJ5UYKwsnUp7jdJ69nAFFJ2wjBuBMqAAt8rm6kVDyhSBS3Cf68Ojzv4gDBH4pkozHA7RICbG5ctXbMlYCI5O+zIAoNcqdafOzk5xfGxgAqVTrFOp/z/86X/E2qbRKvnP/+m/4MGxOaG99Hs/Q2vNVI2yVGI6MSef44N76LZNdaazvIrnr5kTdDY/QzS+tfA17rRfs1UxznnRiwqvInamtUaekq2HH0D6poJxktxEkhO7kXm25F3VyMmy0mfIMGvoZMhSqIxYLu4OOmLP3HMNSLdg1EhbVc2yDIVhcfoMGjxAwbAs/12c8OMktnPdE549Gc6iIXb2zJy4tPs9HD82FVbfy+A75LfVbOLwfkGS+BRXyZ18MD6CJP+9nQuriOm3ntz7GBk1A49vfYw+wS0rrTUU1fJWs4G0R7ohAlDu4tXWwGtBK1p2uYRLIoSu69gxee/4CHu7xhtreXWt9JqLphjdJ/uMZgOffG4EA29+chsx6VAt9XtWMG6938fR0MzXh4MI3XaD7meKEbUPNCcMLkG3v//T17G+acZqNEvRpPmaJQnOJovZoEiVg1OrQRA2LEPu7u27cHjhvRei1zOPu90O7u2bytb2Xgtzqiq5jodMFugC4FDViDkMxwNyrc8ZDg/MuN7YXkNOza5xmlsPNyFyeNR+MY8TKNJqaocuZELaOD6H9wz+i1Xm4a1btyyMNZ1OcXhoxuBgMLCQVp7nlpkVx7FdY6raWq1Wy1Zvms2mrcZU0QrHcey/q2SAanNyFVLkFT/CZxUe5Jyj3zfEGN/3sVRoDQkHnPbFTreDQ2WExSa9Hn7yk5/Ya1xbM+vl6ekQJ8en9P09e+3RPLY+fvN5jjExIF03LPXpwGwVSwiFJCV2bJZhOjO/bxiGaHXNmG24DnznnwBpcZ5adUyVZQjJ2j0MBIpb58lS8Esn0rKlmMPhkIeN9BkyUMe6q6w4YS4TTKdmIn3+5U20qAs7YwIJM5Ow0epAa/JCcTkkwU8KwHRW+KVIq+Tsex4saLhATKIEDl2jI8rSIOPcJiGMlQOHs/NU7vIRg08Y/HN7O3ZiqfxNqLfM50+jOUZj8sZKE1vizWRu+1463Y7F5nf29rC8ZTZXEbRxSPvHUZpg/gxq0oZ+TjsJYxUxsPIxWEk/L64HMAJ8nHYwlmucnZrBe3Drc7Qy8/0vdfbQowVGV5gEjDHboa84rD+XEEZps/x2uvgKFXPKhS8PK/1N6581m86QFt5PWiGkTWRtbQ2rq9RzJDUekHHryWBgYQ5eUSxlvLwXWdpCHNHECzm+/yMzsX/16/dw9Nh8zpXXfogTojbLNMHqipnwvh/g3i3DxptOB1heNs+vbl3CBlGAF4lYndkNg4EhL1SUpVvCklqB88LzLcM8GtJrUuSFDxdL4YiC5VKafiqlziVOxfMSKTg9n2CGRJnPlIohIzaQVBJIS/aWLNiNKl8QCDHheZ6FeNK0FPPzXM+qccdxbMvZQngACAKRp1jdoETXC8BpDRBujFbXrBMf/vZ/4P51c+DoLS9he8f0KDhZhpCUmV05hZ6a5MeJjpEl1P+RdtGgEr/OFZrESIlkhpw/A0sLDjpNakhypE384+kcWWbu53NXnrO190dHh/BcsxEGvVWkJNIZJQ/wJ3/4zwEAS502fveBgeEm8xj3H5s5un9wiGuXzfrhOgp9kgnZWevjwcF9AMBoMscK3QfP95ETpNT0PRzQ58zniV3bnhYKDFOC8B3hIqH7d/DwCG3aNJV2MCamYxb7uPM5Cc6+cAFhk5LcTGNOyvKCO+B0n86mZ1aBWTgan31iILsLO21MZpSoTmcAjRHfD0vVeB4goD1MJzMcPjS/eX91C0dHi+8Z1b6d+XyOD8ghwPM8O4dms5lVSK560AkhbCLfarUsdOX7/jkGVjXhKeai53nnPqdQck6S5FyvTvF6x3H+EWtr0Tgbz7BGLQvHjx6i2SBx3lYLgUf7cc7hESP3B6+/hqUWKUv3O/ju974LABidjfH5TcM++83ffwhO+/pwMMKd47sAgEZrCcv9NfrL3H5/xljhO4rAceHTYW42m1lG2GQygxA03hoBGnQ/vylqSKuOOuqoo4466vjWx1PS9hxFYh8EDEAhmOTCCYhp0GwjJx0HlY6tG63vcnikrxAID1MqNfFAIqWylsMFVlZNOSpLszKzcxQ8TkJy8RjQ5M5a0Y0x7u0me80ybUuY83kEIRY/cX00GKFNUFrbFQjpZOAJDo+yZs6ZLalzlHUdDWX/xVlZHXJdB6++Ypp4X3n5JWiCAlOVI86rPkPmGhOtIanpVnEHGaW1Z2mGLyPz+jSdIaFrzyWe6eRcbf9lsCiWvSbz/PlPtBWtlOFo35yiTgYPsX/HCDDe+/wmVqjkfeVtDw4NFFV5L2PsfKWmeF5rewHmYcHM0ngW3ZYijo6ObTNcu9NAQ1JlMZdIqaS+u7uLNrHcppMptsn3rBEEVngwzxLMqWoomAO/0LpgAilVuaKTISSdEy5eeRHXXn3NfObwDI8O7gIAOi0f8cRUbzrLa3jhmvGEGh0/wqMH5jWu5+D5q9cWvsYbB+/Z05rrugCV4BlnVrTLcRz4BCc6WiBn5CMW5EVfHzSTdjxWKzzV8ndVMFBDQRal5NkdDLVpXkxzjVjRWFbS6mJwxqGLcZrLZ2qUTJIEGbHqhCPOuT4XDZSMMQsPeI5vmXQyyQC6LsdxoQhDT/UZNrfMON3c3AA/KxzeczgT4/PmQCElJqVwOc5OSG8nGmJGN+541MKl1cLpnlkR1sB14eaLW0vkUlpmURAIu3amSYowKHTOOHyCyZRUcHxzD9fW1hARa/PhySn+nsQDGzrGUtP87j96fReaKkLpLMLWkpkLg8kU+0fmpH399D4+u2Uqk92tHaxeM3YVw+EQE4LATCWkZAo1O4sJ86UZhyJW2TzKMaPm6KXVZSPYCODkdI4Pf1dAPxMcE+Txwft3sL1r7nGSxHY9bTUb0MrcszSX6NPv8PKrW8hJ4C7LU2yQHdEGZ4V9FpI4w3xO1XYwdLvmvgoILK2YSoXnuRDi2dogCrgqSRIMSXuuautQbSQWQpyzmSgeB0FgWdCG2WTGmuM4tgpUXQ855xaGdhzHVplc163A0WWTc57ndv5Vv89C4bZwcmrmRMNrYUjIxI0vbmGlZyrly50+Ckv7/vOXkSemovXD3/sxXPKm22j0sUtEnSzT+M27htXcardx+66B9Fe3duCRyGur4WNjbcXek6ISf3R0gna7IG6UVS80BVL6fZ1Gw7J8vyme0sMTgJOhXaMZIM9KpoTS1LPhw5bWkJWmfUwpZNQdn+Y5ZtQHkjsAJ2pakqWICH5YXV6CT2XonAFzKm0PdIKIri3VmTUmdFBi3q7rIKHBplRsRf8WiZvzKTxqpmkJAYcWTa41AhKMCz0HfjEYOYNLg8iznT0G6ipK7UqpihlaBlV4F9F/gOkaSqgcGCmFmDKDuYoQEVyVSmXp2zrPLLTAtUZGcBhefvPpF3lOUlnbxKOK9wLnJ1dROtUMFj75+9++j3v3TN9AEwzNNYPxrqxvWgzqq9iy/Wxd/t1qf86zyWF9fRyfHNqy9fEgwxqJ/l259DzaTVPifPz4CBHR/TudDqbUVzU4OraKrlme2OSQMWEFy84mY0QEN2gmrGzA+kofjH6rRwf3EdGmqZIZusumHDw5G1il105vCTNKrpjMrdHnIjGXA7gEV3nCR4U5DkXlfo/5UEWvmdTWMwspoAkBFaIUSsuy8pBRXZSrnllaawu1SJYj02bTyrRESplzpjJkNLK1gjVrzLIMDhbvG0iSxCo5u3C/1jeo0WiU/QpwkRPVOc+VTXg8z4UsfkdHACQw1xLAlOQxHp8OsfeCYTOtb6zhjCbI9PQEOa0lYeiAOwVEmCAp+kvgkPyF6U0TYvFRLISwY37/1m00aS2M0wxeaMbqaDLH8oqZW1ub2zjcN4v++GwITVDr7qVdCxH9r5//HHd+Y1RxL3z0ObZ3TU/ahY01jOlwefv+A3x+20BHUZYhIqbmz956HXPy4YqjBBmtu44GuKC11k8RkDL60+LhwzNMxsS4iVJM5yTA2vYRtMzzjw5G2L9D8gy7AZ5/0SjId7s9ULsSfL9V2ayZ9TpLc42cxtcPf3IZgqjKna4PImfC9VzrqZQGwAqpUKdZAl5YBHAfnOzBXCHg8sWV66fTqU02DLxCvYmc2+/sed45aL9IbMIwtGPZdd1zzxeP8zy3CY/ruvb1cRzb11QPElmWnTMwtWu31hYyi+P4nCji00J4bQiY927ubGNIjMaziGNOa8bZowdYos+fJgnWSHak2WzhwaEZs59cv44fvfNjAMBbb72B21+aJEdghk0S222GAToEh/3oR2/jO6+YgyAX3Iox/t3Pf4mjI5NY9ld6ePzYsOG63RaOKRmDzJ7aC1FDWnXUUUcdddRRx7c+nuyW3mlCJSZjOjp6BEHMqVazgSg2J6UknSIl8UCX+eDUWKmVwoxOX6fzgW0kjbMUik6GbuhDEqySSWkhJGhhRYziTIKTloPvAoWETJ5r29iYprmVIReCPZMrbDQaIaKkcKQ0lHWPFnCKbDoWcIryJINlrgnOUaBCDtP2NYzx0v8kTsDoRCo5tw2dUgGSGkkzrZFVKnEF5KBkipyuMZlNoeiEKWcz5KTngj/4N0+9xuppQAjHss+qmg1V6fFzAldco0mnzd2rryOW5t562RgesdIkF6Xw41f/eFH5qYgcfvU1zyp7/tXwPA5N/L1mYwk+NSYeHR/hZGhOJp1ez7oaj0YjJHTybbWaSKmCkaapZRZlaVmd0vM5ipKK32hiRifi0ekQOiF4xXWthg/TpZuykhpH5LGmlcLSmikHCyGsBcYikaZzKGL3KJ1B0dxSTMKh8cV4Dk3NvXmWged02nQDW7nKuGGmAeTPVWlwLIIxZkvnWimookHekUiowppLiUyVbC/L/EozCKpoaSWRJ4vDPWGjae9zlqUW6vRcx8JYRk/EfNc0y20zvswTcDriJwBi8sMKPI4+VZnUaIzxmRkPriPQpOrN5OgIE/KsGxzeBag0v7LUg6Dv43ocoKpBxjVy+rvCdcGyxa1sGGf2dz959BguKSq6vm//1nwWYzY3PmL9Xhe9TsGQ0TijhmqtU+xsG1j2X//RH+IXPyeds0d38LsbNwEA//v963CotDdPU1sFZZzhu28byL3hOYip2imUwlrfwAZ5rhDTfXA8hgyLVQdGw5Gt8gaBgENrz1Lbw+aqmR/j0boVHlxeAfyQ1n3lI5fm3jCmEAQlIymOqMKeaqRZAeVI2zaheQRJFfM8SsFZ6RIuiRXcCHwwUQhwAhFdX44M0lkc0pJSntOyKua67/vnYOevEwYMgsDOtSrMVK0Iua57jo1VVJDSNLV7m5TSVm8Mu7Gci1VvvSKq32eRePHFlzAkUkWiBZyGGYNbu5fx4rXLdCMSHB+acTpNUjQjmh+ZQjQz66vgDmKqzm1sXkB/xcCR44HC5T1j4dJf28Tapmmc/+lPfh+nQ1O9CUMfb7xueMhpkuFv/vrn9vuFDdqblUKPHrcC1zq8f1M8MeE5HZ6gR13zjWaAmLCy6SwptOLAuUaSEktLwipZNv2W9d1x3ABpTB33WoPTJOSqlLOaJTE0iQrGGTAkIaJMM0hpkivXE3aAO44LmReKrrALpVISUj7ZIr4aDz/+EMwnaCwIEBA8x4MAOqRSqHCtSCAX3HoOccZt8uNwWLYPGLcbTDpPUHDyHc8rN31dKuQqmUPTppLJFJLuJ+IIumA8RBEySnj4fAaHGACLRFEGLf6uSxtDVa2z+PdXn+fcwcaGGYw//vESdojR4SLHSt8sWkG7Yz2loNlXEpp/TDNnsOiD+ZvF4yrUpRfv5+l2+ugSNXEeTTCm/pnJbIogMIvRPI5tedTg5dQXsboClxhemdTI8mLhiC0mnynAp/6KNgQeEcNFQKNR0Ns5g0cQlcN9yLTwdlMQNAm5lnAds0gxwTE6OV7o+gBASw1ZUPl1hlyXpr6MWo0ycEgSG0yzzG6gGsw+lig3Z11hpVVZWoyxkr0l81Lp1RXIZdFTllpVU1XxrlNZXtLnv/L/nhbjyRRNgiDNwclqcNvNIIpiZJmZE7lCyR6Sqd1shuORheGmWmNZmMWaq9AyRJliOHls+pG67QZimmcyjRESLbbXXUJK84xxjpTmZaYzSIJVpNIYjRajbAOAVjlOH5FAabMDXbBRG11kxRQCR0CsmE6rh6WOSYruPchtvxZ3BOYkyNppePiTf/dH5ntGt5BSUvSL397Bbz80tOHNTh/LfUoCVtZw5WXTV+YLH4OB6WVaXlmxNf/T8THaPaL2O4E97D4tnntuFwn1JXEOMKKKa1VCqc3WBkBwWRxHVqgwSTL4BctQpmWizYEW9RD5mcKcNtMsy+x4jKYJGqHZTD3PBysO3uCYFWuo46LdpsOz78BzCsFT9UyHLsbYuUSlSE6MIF6ZqFR7coq51W637Viu9vxUvbG+6cDuuiXMm6ZppWfUtQcCrbV9fxAEdu76vn/ubzwt/vTf/wGOBuZQfXo2wTGtVdF8HW7R65cyBATDer6PM+p/nM9jbGxQj2S7Yxm0jEkoTQf4ZGbYiAAu7j0Hh/bgNE4Qzc19effX7+ElMp9++eVXcPOG6Vnbv7dvveamwyGe2zV/K2y1cXj6ZOZrDWnVUUcdddRRRx3f+mD/VDihjjrqqKOOOuqo4//1qCs8ddRRRx111FHHtz7qhKeOOuqoo4466vjWR53w1FFHHXXUUUcd3/qoE5466qijjjrqqONbH3XCU0cdddRRRx11fOujTnjqqKOOOuqoo45vffwfuNewfncKDTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据集中的一些用例\n",
    "# 每种类型显示几张训练集中的图片\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# 把数据分成训练集、验证集和测试集。\n",
    "# 另外，我们还会取训练集的一个子集作为开发集，\n",
    "# 在开发时使用它，可以让代码跑的快一点。\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# 验证集为原始训练集的后 num_validation 个数据\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# 训练集为原始训练集的前 num_training 个数据\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# 从训练集中选区一个小的子集作为开发集\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# 测试集为原始测试集的前 num_test 个数据\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# 预处理：把图像数据压缩成一行\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# 健全性检查，打印出数据的 shape\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# 预处理：减去图像均值\n",
    "#   PS：图像均值（image mean）为同一通道同一位置像素的均值，\n",
    "#       这些均值又可构成一幅新的图像，就叫均值图像（mean image）\n",
    "# 1：计算训练集图像均值\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # 打印出少部分元素\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # 可视化均值图像\n",
    "plt.show()\n",
    "\n",
    "# 2：减去均值图像\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# 3：每行追加一个值为 1 的偏差，这样 SVM 只要对一个权重矩阵 W 进行优化就行了。\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM 分类器\n",
    "\n",
    "本节你要写的代码全在 `cs231n/classifiers/linear_svm.py` 中。\n",
    "\n",
    "可以看到，我们已经预先写好了 `compute_loss_naive` 函数，它使用 `for` 循环来评估多类 SVM 损失函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.012240\n"
     ]
    }
   ],
   "source": [
    "# 对简单版本的损失函数进行评估\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time  \n",
    "\n",
    "# 生成一个全为小数字的随机 SVM 权重矩阵\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面函数现在返回的结果 `grad` 还是 0。推导 SVM 损失函数的梯度的计算方式，然后在 `svm_loss_naive` 函数中编写对应的代码。编写完后 `grab` 就能够计算出来了。\n",
    "\n",
    "为了检查写得对不对，你可以计算数值梯度，然后比较数值梯度和上面的解析梯度是否一致。下面我们已经写好了实现这个功能的代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -23.569544 analytic: -23.569544, relative error: 2.189272e-11\n",
      "numerical: 38.069041 analytic: 38.069041, relative error: 1.090077e-11\n",
      "numerical: 25.874948 analytic: 25.874948, relative error: 6.243452e-12\n",
      "numerical: 8.065577 analytic: 8.065577, relative error: 1.816699e-11\n",
      "numerical: -9.927214 analytic: -10.002680, relative error: 3.786570e-03\n",
      "numerical: 11.926669 analytic: 11.926669, relative error: 4.256926e-12\n",
      "numerical: 12.286146 analytic: 12.366282, relative error: 3.250622e-03\n",
      "numerical: 2.567602 analytic: 2.577426, relative error: 1.909309e-03\n",
      "numerical: -17.189051 analytic: -17.189051, relative error: 1.700901e-11\n",
      "numerical: 16.469719 analytic: 16.469719, relative error: 5.329808e-12\n",
      "numerical: 9.083778 analytic: 9.083778, relative error: 3.133282e-11\n",
      "numerical: 3.969649 analytic: 3.969649, relative error: 3.367143e-11\n",
      "numerical: 7.413105 analytic: 7.324476, relative error: 6.013806e-03\n",
      "numerical: 6.021432 analytic: 6.021432, relative error: 1.479011e-11\n",
      "numerical: 2.909019 analytic: 2.909019, relative error: 3.330952e-11\n",
      "numerical: 31.445733 analytic: 31.445733, relative error: 1.119031e-11\n",
      "numerical: 12.633288 analytic: 12.667309, relative error: 1.344675e-03\n",
      "numerical: -3.960199 analytic: -3.947544, relative error: 1.600278e-03\n",
      "numerical: -3.984388 analytic: -3.976283, relative error: 1.018209e-03\n",
      "numerical: -25.583188 analytic: -25.583188, relative error: 3.989847e-12\n"
     ]
    }
   ],
   "source": [
    "# 实现了梯度计算后，重新执行下面代码\n",
    "# 并用我们事先提供的函数进行梯度检查\n",
    "\n",
    "# 计算参数为 W 时的损失和梯度\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# 随机选取几个维度计算其数值梯度，然后与你自己计算的解析梯度进行比较。\n",
    "# 两种梯度的结果应该在所有维度上都几乎相等。\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# 开启正则化后重新进行梯度检查\n",
    "# 你没有忘记正则化梯度吧？\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**相关问题 1**\n",
    "\n",
    "有时梯度检查会在某个维度上可能会失败。造成这种情况的原因可能是什么？需要特别关注这样的问题吗？有没有梯度检查在某个维度上可能失败的简答例子？如何改变这种情况发生频率的边际效应？*注意：严格来说，SVM 的损失函数是不可微的*\n",
    "\n",
    "$\\color{blue}{\\textit 答:}$ *在此作答。*  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.012240e+00 computed in 0.140246s\n",
      "Vectorized loss: 9.012240e+00 computed in 0.002147s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# 接下来实现函数 svm_loss_vectorized；这次只计算损失；\n",
    "# 稍后再计算梯度\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.141327s\n",
      "Vectorized loss and gradient: computed in 0.012548s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机梯度下降\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 782.822895\n",
      "iteration 100 / 1500: loss 468.005754\n",
      "iteration 200 / 1500: loss 283.432477\n",
      "iteration 300 / 1500: loss 172.677925\n",
      "iteration 400 / 1500: loss 106.384485\n",
      "iteration 500 / 1500: loss 66.211533\n",
      "iteration 600 / 1500: loss 42.260445\n",
      "iteration 700 / 1500: loss 27.626865\n",
      "iteration 800 / 1500: loss 18.501008\n",
      "iteration 900 / 1500: loss 13.513022\n",
      "iteration 1000 / 1500: loss 10.541172\n",
      "iteration 1100 / 1500: loss 8.040882\n",
      "iteration 1200 / 1500: loss 6.856297\n",
      "iteration 1300 / 1500: loss 6.958373\n",
      "iteration 1400 / 1500: loss 5.813578\n",
      "That took 3.839701s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, batch_size=256, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.380633\n",
      "validation accuracy: 0.390000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 793.934944\n",
      "iteration 100 / 1500: loss 473.127211\n",
      "iteration 200 / 1500: loss 287.607523\n",
      "iteration 300 / 1500: loss 175.654515\n",
      "iteration 400 / 1500: loss 107.438447\n",
      "iteration 500 / 1500: loss 66.561350\n",
      "iteration 600 / 1500: loss 42.011958\n",
      "iteration 700 / 1500: loss 27.356167\n",
      "iteration 800 / 1500: loss 19.078282\n",
      "iteration 900 / 1500: loss 13.981613\n",
      "iteration 1000 / 1500: loss 10.214453\n",
      "iteration 1100 / 1500: loss 8.829194\n",
      "iteration 1200 / 1500: loss 6.743490\n",
      "iteration 1300 / 1500: loss 6.924272\n",
      "iteration 1400 / 1500: loss 5.981164\n",
      "iteration 0 / 1500: loss 1543.288019\n",
      "iteration 100 / 1500: loss 560.497776\n",
      "iteration 200 / 1500: loss 207.581562\n",
      "iteration 300 / 1500: loss 78.798437\n",
      "iteration 400 / 1500: loss 32.154985\n",
      "iteration 500 / 1500: loss 15.567631\n",
      "iteration 600 / 1500: loss 8.956890\n",
      "iteration 700 / 1500: loss 7.232767\n",
      "iteration 800 / 1500: loss 5.874828\n",
      "iteration 900 / 1500: loss 6.173525\n",
      "iteration 1000 / 1500: loss 6.155438\n",
      "iteration 1100 / 1500: loss 5.764659\n",
      "iteration 1200 / 1500: loss 5.863960\n",
      "iteration 1300 / 1500: loss 5.764221\n",
      "iteration 1400 / 1500: loss 5.466719\n",
      "iteration 0 / 1500: loss 784.640802\n",
      "iteration 100 / 1500: loss 1325.930616\n",
      "iteration 200 / 1500: loss 1635.313677\n",
      "iteration 300 / 1500: loss 1400.088594\n",
      "iteration 400 / 1500: loss 1477.587542\n",
      "iteration 500 / 1500: loss 1212.458639\n",
      "iteration 600 / 1500: loss 1969.298803\n",
      "iteration 700 / 1500: loss 1547.732840\n",
      "iteration 800 / 1500: loss 1453.633938\n",
      "iteration 900 / 1500: loss 1339.267174\n",
      "iteration 1000 / 1500: loss 1430.233913\n",
      "iteration 1100 / 1500: loss 2032.399725\n",
      "iteration 1200 / 1500: loss 1384.106482\n",
      "iteration 1300 / 1500: loss 2025.180812\n",
      "iteration 1400 / 1500: loss 1851.324437\n",
      "iteration 0 / 1500: loss 1573.781689\n",
      "iteration 100 / 1500: loss 1002326637192557597531942440864451657728.000000\n",
      "iteration 200 / 1500: loss 165676565572899328869635362353497782424975814119708835140650700244311343104.000000\n",
      "iteration 300 / 1500: loss 27385009398645786025026490582689985836542710157275499239569023167274476170873570750870506016379967433760309248.000000\n",
      "iteration 400 / 1500: loss 4526522729214455011581192104244463792440308507342634184402350538515040704847837404713735994655318795460073955189114609683906053956623243472273408.000000\n",
      "iteration 500 / 1500: loss 748197954575407205866575971739814997867365906277816090927931376283269411695560127329488582892849549993777392319070758815777472670309724066832446662635783129418803650094582009430016.000000\n",
      "iteration 600 / 1500: loss 123671129632872179272082533930528825442935246111452980942575372843229881979682510543503802121672880827294796756858356751606837563574792641587869213416529134457338232430184981096297312140929187467195008735331548659712.000000\n",
      "iteration 700 / 1500: loss 20441847255984725795697899733565839709607592678970170298789643471826898337202416820902562187465562338864358522711290200751482944874806993690947717516805673898420806283262093594862975202789356908660632897035057611634031978258174891458023599663944302592.000000\n",
      "iteration 800 / 1500: loss 3378873634270898681413720903618099137391226454976742548579490859569998240995627072959181554879078527875176013811232481373227545503273559381412870667066440312832682310094013851606069201790030706139032472340481996775056024910629091812921709157027369800188587783030830639134826437283938304.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Volumes/Macu/moilk/py/cs231n/assignment1/cs231n/classifiers/linear_svm.py:88: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss += reg * np.sum(W * W)\n",
      "/Volumes/Macu/data/anaconda3/envs/cs231n/lib/python3.7/site-packages/numpy/core/fromnumeric.py:90: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/Volumes/Macu/moilk/py/cs231n/assignment1/cs231n/classifiers/linear_svm.py:88: RuntimeWarning: overflow encountered in multiply\n",
      "  loss += reg * np.sum(W * W)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 900 / 1500: loss inf\n",
      "iteration 1000 / 1500: loss inf\n",
      "iteration 1100 / 1500: loss inf\n",
      "iteration 1200 / 1500: loss inf\n",
      "iteration 1300 / 1500: loss inf\n",
      "iteration 1400 / 1500: loss inf\n",
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.378673 val accuracy: 0.381000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.368449 val accuracy: 0.384000\n",
      "lr 5.000000e-05 reg 2.500000e+04 train accuracy: 0.149265 val accuracy: 0.137000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.058531 val accuracy: 0.058000\n",
      "best validation accuracy achieved during cross-validation: 0.384000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        \n",
    "        results[(lr, rs)] = (train_acc, val_acc)\n",
    "        \n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
